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A Computational Procedure for the Recognition and Classiﬁcation of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

: Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study inﬁltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classiﬁcation of these diseases. Neuroph was used to perform the training of a CNN network that recognised and classiﬁed images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves. The northern corn leaf blight ( Exserohilum ), common rust ( Puccinia sorghi ) and gray leaf spot ( Cercospora ) diseases were chosen for this study as they affect most parts of Southern Africa’s maize ﬁelds. Abstract: Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognised and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves. The northern corn leaf blight ( Exserohilum ), common rust ( Puccinia sorghi ) and gray leaf spot ( Cercospora ) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields. the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize

Introduction
Gray leaf spot of maize is a maize disease caused by the Cercospora zeae-maydis fungus. It is now seen as one of the most significant yield-limiting diseases of maize (corn) worldwide, posing a serious threat to maize production in many areas of the eastern United States and, more recently, in large areas of the U.S. Corn Belt and Africa. Typically, its symptoms are observed on the lower leaves [1]. Figures 1  and 2 show the stages of gray leaf spot of maize in terms of maturity.

Introduction
Gray leaf spot of maize is a maize disease caused by the Cercospora zeae-maydis fungus. It is now seen as one of the most significant yield-limiting diseases of maize (corn) worldwide, posing a serious threat to maize production in many areas of the eastern United States and, more recently, in large areas of the U.S. Corn Belt and Africa. Typically, its symptoms are observed on the lower leaves [1]. Figures 1 and 2 show the stages of gray leaf spot of maize in terms of maturity.   Common rust maize disease, otherwise caused by the Puccinia sorghi pathogen is favoured, by cool temperatures (16-23 °C) and high relative humidity (100%) [2]. The functional leaf area and photosynthesis are reduced by the disease lesions. Spots are found on both upper and lower leaf surfaces [3]. Figures 3 and 4 show early common rust lesions and more advanced disease development, respectively.    Common rust maize disease, otherwise caused by the Puccinia sorghi pathogen is favoured, by cool temperatures (16-23 • C) and high relative humidity (100%) [2]. The functional leaf area and photosynthesis are reduced by the disease lesions. Spots are found on both upper and lower leaf surfaces [3]. Figures 3 and 4 show early common rust lesions and more advanced disease development, respectively. Common rust maize disease, otherwise caused by the Puccinia sorghi pathogen is favoured, by cool temperatures (16-23 °C) and high relative humidity (100%) [2]. The functional leaf area and photosynthesis are reduced by the disease lesions. Spots are found on both upper and lower leaf surfaces [3]. Figures 3 and 4 show early common rust lesions and more advanced disease development, respectively.  Northern corn leaf blight is a maize disease caused by a fungus called Exserohilum turcicum. Cool to moderate temperatures and high relative humidity favour the development of the northern corn leaf blight. The symptoms are recognised by the relatively large gray cigar-shaped lesions that can develop on leaves [4]. The changing stages of northern corn leaf blight due to a fungus concentration are as shown in Figures 5 and 6.  Common rust maize disease, otherwise caused by the Puccinia sorghi pathogen is favoured, by cool temperatures (16-23 °C) and high relative humidity (100%) [2]. The functional leaf area and photosynthesis are reduced by the disease lesions. Spots are found on both upper and lower leaf surfaces [3]. Figures 3 and 4 show early common rust lesions and more advanced disease development, respectively.  Northern corn leaf blight is a maize disease caused by a fungus called Exserohilum turcicum. Cool to moderate temperatures and high relative humidity favour the development of the northern corn leaf blight. The symptoms are recognised by the relatively large gray cigar-shaped lesions that can develop on leaves [4]. The changing stages of northern corn leaf blight due to a fungus concentration are as shown in Figures 5 and 6.  Northern corn leaf blight is a maize disease caused by a fungus called Exserohilum turcicum. Cool to moderate temperatures and high relative humidity favour the development of the northern corn leaf blight. The symptoms are recognised by the relatively large gray cigar-shaped lesions that can develop on leaves [4]. The changing stages of northern corn leaf blight due to a fungus concentration are as shown in Figures 5 and 6. Common rust maize disease, otherwise caused by the Puccinia sorghi pathogen is favoured, by cool temperatures (16-23 °C) and high relative humidity (100%) [2]. The functional leaf area and photosynthesis are reduced by the disease lesions. Spots are found on both upper and lower leaf surfaces [3]. Figures 3 and 4 show early common rust lesions and more advanced disease development, respectively.  Northern corn leaf blight is a maize disease caused by a fungus called Exserohilum turcicum. Cool to moderate temperatures and high relative humidity favour the development of the northern corn leaf blight. The symptoms are recognised by the relatively large gray cigar-shaped lesions that can develop on leaves [4]. The changing stages of northern corn leaf blight due to a fungus concentration are as shown in Figures 5 and 6.   In the literature, there are several algorithms of machine learning that were used for the recognition and detection of plant leaf diseases [5], utilizing Caffe, a deep learning framework developed by Berkeley Vision and Learning Centre for the recognition of plant leaf diseases by means of CNN. With feature extraction methods based on Open CV, the experimental results of the developed model achieved between 91% and 98% precision, and 93% on average for separate class tests [5].
By using deep learning methods, a public dataset of 54,306 images of diseased and healthy plant leaves was collected under controlled conditions and used to train a deep convolutional neural network to identify 14 crop species and 26 diseases. The feasibility of this approach was demonstrated by a trained model that achieved an accuracy of 99.35% on a held-out test set [6].
A study was carried out to apply the artificial neural network (ANN) analysis technique for discriminating and classifying fungal infections in oil palm trees [7]. Raw, first and second derivative spectra radiometer datasets were used at an early stage. These were acquired from 1,16 spectral signatures of foliar samples, in four disease levels (T1: healthy, T2: mildly-infected, T3: moderately infected, and T4: severely infected) [7].
A web based tool was proposed that helped farmers in the identification of fruit diseases by uploading fruit images to the system. Already trained datasets of the pomegranate fruit were used by the system. The images to be analysed were given by the user to undergo several processing steps in order to detect the severity of the diseases by comparing them with the trained dataset images. The experimental results of the proposed approach demonstrated 82% accuracy for the identification of the pomegranate disease [8].
Very little is known about the implementation of CNN for plant disease recognition built on a framework that utilises general user interface (GUI) to train the network by use of raw images with feature extractions embedded in the program's library. The main objective of this study was to design a system that would recognise and classify the maize leaf diseases out of healthy leaves by means of facilitated CNN. The development and novelty of the proposed model lay in its simplicity; background images and healthy leaves were in accordance with other classes, empowering the model to distinguish between diseased leaves and sound ones or from the environment by utilizing deep CNN.
The rest of the paper is organised as follows: The Materials and Methods are presented in Section 2; achieved experimental results and related discussions are presented in Section 3; Section 4 discusses the results; and finally Section 5 draws up our conclusions of the study.

Related Work
The use of machine learning algorithms for the detection of plant leaf disease is dominant in the literature. Machine learning models for the prediction of these plant leaf diseases were found to differ in accuracy. Various techniques are at present being used for the recognition of plant leaf diseases by the application of computer vision. One of them is disease detection by colour feature extraction from images. CNNs are known to outsmart the classification and identification of images by use of a colour feature extraction. For such reasons, Sladojevic et al. performed the deep CNN training for plant disease recognition and classification [5]. The test results on the created model accomplished accuracy somewhere in the range of 91% and 98%, for separate class tests, 96.3% on average. In the literature, there are several algorithms of machine learning that were used for the recognition and detection of plant leaf diseases [5], utilizing Caffe, a deep learning framework developed by Berkeley Vision and Learning Centre for the recognition of plant leaf diseases by means of CNN. With feature extraction methods based on Open CV, the experimental results of the developed model achieved between 91% and 98% precision, and 93% on average for separate class tests [5].
By using deep learning methods, a public dataset of 54,306 images of diseased and healthy plant leaves was collected under controlled conditions and used to train a deep convolutional neural network to identify 14 crop species and 26 diseases. The feasibility of this approach was demonstrated by a trained model that achieved an accuracy of 99.35% on a held-out test set [6].
A study was carried out to apply the artificial neural network (ANN) analysis technique for discriminating and classifying fungal infections in oil palm trees [7]. Raw, first and second derivative spectra radiometer datasets were used at an early stage. These were acquired from 1,16 spectral signatures of foliar samples, in four disease levels (T1: healthy, T2: mildly-infected, T3: moderately infected, and T4: severely infected) [7].
A web based tool was proposed that helped farmers in the identification of fruit diseases by uploading fruit images to the system. Already trained datasets of the pomegranate fruit were used by the system. The images to be analysed were given by the user to undergo several processing steps in order to detect the severity of the diseases by comparing them with the trained dataset images. The experimental results of the proposed approach demonstrated 82% accuracy for the identification of the pomegranate disease [8].
Very little is known about the implementation of CNN for plant disease recognition built on a framework that utilises general user interface (GUI) to train the network by use of raw images with feature extractions embedded in the program's library. The main objective of this study was to design a system that would recognise and classify the maize leaf diseases out of healthy leaves by means of facilitated CNN. The development and novelty of the proposed model lay in its simplicity; background images and healthy leaves were in accordance with other classes, empowering the model to distinguish between diseased leaves and sound ones or from the environment by utilizing deep CNN.
The rest of the paper is organised as follows: The Materials and Methods are presented in Section 2; achieved experimental results and related discussions are presented in Section 3; Section 4 discusses the results; and finally Section 5 draws up our conclusions of the study.

Related Work
The use of machine learning algorithms for the detection of plant leaf disease is dominant in the literature. Machine learning models for the prediction of these plant leaf diseases were found to differ in accuracy. Various techniques are at present being used for the recognition of plant leaf diseases by the application of computer vision. One of them is disease detection by colour feature extraction from images. CNNs are known to outsmart the classification and identification of images by use of a colour feature extraction. For such reasons, Sladojevic  A study in comparison of support vector machine (SVM) and ANN, was performed by Pujai et al. [9]. Algorithms for colour extraction and texture features were developed, which were thus used to train SVM and ANN classifiers. The study presented a reduced feature set based approach for the recognition and classification of images of plant diseases. The results revealed that the SVM classifier was progressively reasonable for identification and classification of plant diseases. An SVM classifier was 92.17% than the ANN classifier that had an accuracy of 87.4%.
Recently, wheat disease detection through leaf image and data processing techniques is used extensively to assist farmers in monitoring the big plantation area. Researchers such as Dixit and Ema applied SVM for the recognition and classification of wheat diseases. In their study, they mentioned in detail the key issues and challenges in wheat leaf detection by means of SVM [10].
The purpose of this study is to model a facilitated GUI based CNN for the recognition and classification of maize leaf diseases. The use of the image recognition feature in the Neuroph Studio framework enabled the design of CNN with feature extraction features embedded in the program's library. The overall system's accuracy showed a progressive 99.9% for the classification of northern corn leaf blight (Exserohilum), 91% of gray leaf spot (Cercospora), 87% of common rust (Puccinia sorghi), and 93.5% of healthy leaves using a maize data set of 100 images for each disease class, and 100 images for healthy class.

The Concept of CNN
The regular NN (neural network) is not equipped for managing the images. If a regular NN were to be used with the images, then each pixel of the image would have to be connected to its neuron resulting in a network which would be computationally expensive. CNN handles the images in various ways, yet, at the same time, it pursues the general idea of regular NN. A study in comparison of support vector machine (SVM) and ANN, was performed by Pujai et al. [9]. Algorithms for colour extraction and texture features were developed, which were thus used to train SVM and ANN classifiers. The study presented a reduced feature set based approach for the recognition and classification of images of plant diseases. The results revealed that the SVM classifier was progressively reasonable for identification and classification of plant diseases. An SVM classifier was 92.17% than the ANN classifier that had an accuracy of 87.4%.
Recently, wheat disease detection through leaf image and data processing techniques is used extensively to assist farmers in monitoring the big plantation area. Researchers such as Dixit and Ema applied SVM for the recognition and classification of wheat diseases. In their study, they mentioned in detail the key issues and challenges in wheat leaf detection by means of SVM [10].
The purpose of this study is to model a facilitated GUI based CNN for the recognition and classification of maize leaf diseases. The use of the image recognition feature in the Neuroph Studio framework enabled the design of CNN with feature extraction features embedded in the program's library. The overall system's accuracy showed a progressive 99.9% for the classification of northern corn leaf blight (Exserohilum), 91% of gray leaf spot (Cercospora), 87% of common rust (Puccinia sorghi), and 93.5% of healthy leaves using a maize data set of 100 images for each disease class, and 100 images for healthy class.

The Concept of CNN
The regular NN (neural network) is not equipped for managing the images. If a regular NN were to be used with the images, then each pixel of the image would have to be connected to its neuron resulting in a network which would be computationally expensive. CNN handles the images in various ways, yet, at the same time, it pursues the general idea of regular NN. Figure 7 shows an architecture of the CNN. Convolution and pooling (feature extraction): Convolution has a set of learnable filters which are a matrix (width (W), height (H), and depth (D)). The input image is considered as a matrix and the filter is imagined sliding through the input image matrix in order to get the convoluted image which is the filtered image of the actual input image. If a filter is applied on the input image, the result would be an output matrix smaller than the original image. Padding plays an important role if we need to get the same size outputted as the input size. Pooling is another important concept of CNN. Pooling is a form of non-linear down sampling function that in the case of CNNs is applied to convoluted images. Among several non-linear functions that can do the pooling, max pooling is the most common one.
Classification: The input images are classified by a fully connected layer just after the convolution and max pooling layers. Neurons in a fully connected layer have connections to all Convolution and pooling (feature extraction): Convolution has a set of learnable filters which are a matrix (width (W), height (H), and depth (D)). The input image is considered as a matrix and the filter is imagined sliding through the input image matrix in order to get the convoluted image which is the filtered image of the actual input image. If a filter is applied on the input image, the result would be an output matrix smaller than the original image. Padding plays an important role if we need to get the same size outputted as the input size. Pooling is another important concept of CNN. Pooling is a form of non-linear down sampling function that in the case of CNNs is applied to convoluted images. Among several non-linear functions that can do the pooling, max pooling is the most common one.
Classification: The input images are classified by a fully connected layer just after the convolution and max pooling layers. Neurons in a fully connected layer have connections to all activations in the previous layer, as seen in the regular neural network. Figure 7 shows the maize plant diseases that were recognised and classified by a fully connected layer of the proposed CNN.

Materials
The Neuroph framework consists of the Java neural network library and integrated neural network and Java IDE based on the NetBeans Platform-Neuroph Studio. It is an integrated environment to create and deploy neural networks to Java programs. Neuroph supports common neural network architectures, and it is very flexible so one can easily extend it to suit the specific needs. The basic structure of the framework packages is shown in Figure 8.
activations in the previous layer, as seen in the regular neural network. Figure 7 shows the maize plant diseases that were recognised and classified by a fully connected layer of the proposed CNN.

Materials
The Neuroph framework consists of the Java neural network library and integrated neural network and Java IDE based on the NetBeans Platform-Neuroph Studio. It is an integrated environment to create and deploy neural networks to Java programs. Neuroph supports common neural network architectures, and it is very flexible so one can easily extend it to suit the specific needs. The basic structure of the framework packages is shown in Figure 8.

Methodology
A CNN was built on the Windows PC platform. Neuroph Studio framework was used to build a CNN with 50 hidden layers for the recognition and classification of maize leaf diseases out of healthy leaves. The network was trained by using 100 colour images of each disease and healthy leaves with resolution settings configured as 10 × 20 × 3 (height × width × RGB). With the previously mentioned image resolution settings, the CNN was modelled to have 600 inputs through the input layer of the neural network classifier. Of 100 available images for each class to be recognised, 70% were used for training and 30% for testing of the CNN. Back propagation was used as the learning algorithm to train the CNN network.
A mathematical model of the proposed CNN was implemented as shown below: The order taken from the CNN was an order 3 tensor as its input. The input images that were tested for possible diseases were imaged with H rows, W columns, and 3 channels (RGB colour channels). An abstract description of our CNN model is illustrated by Equation (1).
Equation (1) shows how the proposed model of the CNN runs layer by layer. The input was an image of a maize leaf disease or a sound one with order 3 tensors. The was the input to the first layer's input collectively known as tensor W 1. . The output of the first layer was which also acted as the input to the second layer processing. The processing proceeded until all layers in the CNN had been finished, which gave an output of . One additional layer, however, was added for backward error propagation for learning parameter values in the CNN. The last layer was a loss layer. A simple loss function we used is as shown in Equation (2).

Methodology
A CNN was built on the Windows PC platform. Neuroph Studio framework was used to build a CNN with 50 hidden layers for the recognition and classification of maize leaf diseases out of healthy leaves. The network was trained by using 100 colour images of each disease and healthy leaves with resolution settings configured as 10 × 20 × 3 (height × width × RGB). With the previously mentioned image resolution settings, the CNN was modelled to have 600 inputs through the input layer of the neural network classifier. Of 100 available images for each class to be recognised, 70% were used for training and 30% for testing of the CNN. Back propagation was used as the learning algorithm to train the CNN network.
A mathematical model of the proposed CNN was implemented as shown below: The order taken from the CNN was an order 3 tensor as its input. The input images that were tested for possible diseases were imaged with H rows, W columns, and 3 channels (RGB colour channels). An abstract description of our CNN model is illustrated by Equation (1).
Equation (1) shows how the proposed model of the CNN runs layer by layer. The input x 1 was an image of a maize leaf disease or a sound one with order 3 tensors. The x 1 was the input to the first layer's input collectively known as tensor W 1 . The output of the first layer was x 2 which also acted as the input to the second layer processing. The processing proceeded until all layers in the CNN had been finished, which gave an output of x L . One additional layer, however, was added for backward error propagation for learning parameter values in the CNN. The last layer was a loss layer. A simple loss function we used is as shown in Equation (2).
In Equation (2), t denotes the corresponding target value for the input x L . The loss function formula in Equation (2) was used to measure the discrepancy between CNN prediction x L and t. Alternatively, the output prediction of the CNN was given as shown in Equation (3).
The loss layer was not needed in the prediction, but was useful in learning of CNN parameters using a set of diseased and healthy maize leaf data sets as training sets. Our CNN model used the stochastic gradient descent (SGD) in order to learn the model parameters. Instead of outputting a prediction, we compared the prediction with target t corresponding to input. The loss Z was then a supervision signal, guiding how the parameters of the model were to be updated. The SGD way of modifying the parameters was as shown in Equation (4).
where η represents the learning rate. The learning rate was chosen to be 0.01. Equation (5) has a superscript "time" index (e.g., training epochs/iterations).
The activation function used in the convolution layer was the rectified linear unit (ReLU) function. The ReLU can be regarded as a truncation performed individually for every element in the input. The ReLU is presented by Equation (6).
The l-th layer, had inputs that formed an order 3 tensor x l with x l ∈ R H l ×W l ×D l . Thus, for this reason we needed a triplet index set i l , j l , d l to locate any specific element in x l . Based on Equation (6), it is obvious that in Equation (7), where . was the indicator function, being 1 if its argument was true, and 0 otherwise. Hence, we had y is an alias of x l+1 . An alias means that a variable can be reshaped into another form. To be specific, the function max (0, x) is not differentiable at x = 0. Focusing on the convolution layer, the normalised Kernel was used to convolve the input images of both diseased and healthy maize leaves. The convolution operation is explained in Equation (9).
h(m, n) is a filtering mask of size M × N. Each element in this filter mask represents the weights used in the linear combination. It is at this stage where the ReLU activation function was used with the convoluted input images of both diseased and healthy maize leaves in order to bring non linearity. The filter that we used for the convolution of the input images in this study, is as shown by Equation (10).
As previously explained in this study, pooling was used to down sample the convoluted non-linear image in order to form an input signal inputted to a fully connected classifier.

Data Collection and Testing of the CNN
The images to be analysed for possible existence of the maize diseases were captured using a Google Pixel 3 smart phone camera in a maize field and saved in a file located on Google Drive. To analyse the collected data from the maize field, Neuroph's CNN was used at a computer station. Figure 9 shows a fully connected classifier of the proposed CNN. Figure 10 shows how the Google Drive's file was accessed to select one of the field images that was analysed for possible existence of the maize leaf diseases. It can be seen in the result's output window that the common rust disease had a high probability of 0.8 followed by the northern corn leaf blight with a probability of 0.5. By continuously accessing a file downloaded from Google Drive containing the field data, the collected field images were all analysed for possible existence of the diseases.
As previously explained in this study, pooling was used to down sample the convoluted nonlinear image in order to form an input signal inputted to a fully connected classifier.

Data Collection and Testing of the CNN
The images to be analysed for possible existence of the maize diseases were captured using a Google Pixel 3 smart phone camera in a maize field and saved in a file located on Google Drive. To analyse the collected data from the maize field, Neuroph's CNN was used at a computer station. Figure 9 shows a fully connected classifier of the proposed CNN. Figure 10 shows how the Google Drive's file was accessed to select one of the field images that was analysed for possible existence of the maize leaf diseases. It can be seen in the result's output window that the common rust disease had a high probability of 0.8 followed by the northern corn leaf blight with a probability of 0.5. By continuously accessing a file downloaded from Google Drive containing the field data, the collected field images were all analysed for possible existence of the diseases.          Figure 15 shows the iterations during training of the CNN that were executed to minimise the error from 0.275 to a reduced error of 0.001. As can be seen in Figure 15, an approximated total of 150 iterations was executed to reduce the error to 0.01.   Figure 15 shows the iterations during training of the CNN that were executed to minimise the error from 0.275 to a reduced error of 0.001. As can be seen in Figure 15, an approximated total of 150 iterations was executed to reduce the error to 0.01.  Figure 16 shows an assignment of weights to each of the 50 hidden layers that were used to build the CNN for the recognition and classification of maize diseases out of healthy leaves.  Figure 16 shows an assignment of weights to each of the 50 hidden layers that were used to build the CNN for the recognition and classification of maize diseases out of healthy leaves. The results for separate class tests are shown in Table 1. The CNN was very accurate in the recognition and classification of northern corn leaf blight (Exserohilum) with an outstanding accuracy of 99.9%. Some of the test images contained the characteristics of both gray leaf spot (Cercospora) and common rust (Puccinia sorghi), a reason which justified the CNN's under performance when it came to the classification of the two diseases. The CNN's overall accuracy was determined by Equation (11).

Results
The overall classifier accuracy of the CNN was 92.85%. The test results of the proposed CNN model achieved accuracies somewhere in the range of 87% and 99.9%, for separate class tests, and 92.85% on average. Table 2 shows the results of convoluted input images as well as histogram results of the input images through the proposed CNN. The results for separate class tests are shown in Table 1. The CNN was very accurate in the recognition and classification of northern corn leaf blight (Exserohilum) with an outstanding accuracy of 99.9%. Some of the test images contained the characteristics of both gray leaf spot (Cercospora) and common rust (Puccinia sorghi), a reason which justified the CNN's under performance when it came to the classification of the two diseases. The CNN's overall accuracy was determined by Equation (11).
Overall accuracy = ∑ classi f ier accuracies Average × 100, The overall classifier accuracy of the CNN was 92.85%. The test results of the proposed CNN model achieved accuracies somewhere in the range of 87% and 99.9%, for separate class tests, and 92.85% on average. Table 2 shows the results of convoluted input images as well as histogram results of the input images through the proposed CNN.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.

Discussion
These results have further strengthened our hypothesis that the CNN was capable of recognizing and classifying the maize leaf diseases at an overall accuracy of 92.85%. The CNN was trained using approximately 150 iterations to reach a minimal error of 0.01. This proved that the model was quick to learn from the training data. In separate class tests, the model achieved accuracies of 99.9% for northern corn leaf blight, 91% for gray leaf spot, 87% for common rust and 93.5% of healthy maize leaves. These results were obtained by using a batch of 100 images for each disease class, and 100 healthy images to train the CNN. Different images for each batch were used to conclude the feasibility of the research and the obtained accuracy results. This study focused on three maize disease types that were caused by biotic stresses. However, the proposed CNN would also be used to recognise the diseases that could be the cause of the abiotic stresses if it were to be trained with the data collected from the abiotic stressed plants. Future research is proposed to determine the CNN's performance that would be trained with the biotic stressed data and then tested against the abiotic stressed data.
In the literature, there are a lot of machine learning studies that explained the detection of plant leaf diseases. However, none so far has explored the detection of maize diseases in large and open maize fields. By the use of deep CNN built in the Neuroph studio and the Google Pixel 3 smart phone we managed to conduct a study to detect the three types of maize diseases that occurred in the large open maize fields. The maize field was divided into section areas of 10 × 10 square meters and the data was collected using a Google Pixel 3 smart phone from each section area. The base used store the collected data was Google Drive. During the training and testing of the CNN the data were retrieved from Google Drive and used to train or test the CNN. The proposed method eliminates the use computation methods and cameras for data acquisition. Our method is more accurate than other methods that utilise data acquisition computational methods as the data was collected by the user from any angle of the leaves.
Another advantage is that our proposed CNN was built in a GUI platform. This will enable people who are not familiar with high level programming languages, such as Python, MATLAB and C to mention a few, to build a CNN from scratch.

Conclusions
In this study, we have reviewed related works in the literature for the plant leaf disease classification algorithms of machine learning. Neuroph Studio framework was used as an IDE to build a more facilitated deep CNN whereby the convolution and pooling feature extractions were embedded in the Neuroph library. The proposed CNN was trained and tested using datasets from Plant Village's online website. The CNN's overall accuracy of 92.85% proved its feasibility. The CNN was also tested using the data collected for maize and the results approximated to the results we obtained during the usage of the testing data. It is recommended that the researchers who wish to use the proposed CNN in this study use the resolution settings of 10 × 20 × 3 (height × width × RGB). Future research is proposed to determine the CNN's performance when the training and testing are executed with gray scale images.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

text:
Effect of dietary organic acids on nutrient digestibility, faecal moisture, digesta pH and viscosity of broiler chickens

Finishing broiler chickens at a reasonable period of time will guarantee profit maximization which is the target of poultry farmers. According to Ndelekwute et al.1 provision of both quality and adequate feed to broiler chickens is essential for optimum performance. They maintained that quality feed is one which supplies all the nutrients required for productivity while adequate feeding means provision of the daily feed requirement free choice. It has been suggested that access to quality feed and attainment of daily requirement was better ways to achieve faster growth in broilers.2 The quality and quantity of feed cannot guarantee optimum productivity if the feed so supplied is not well utilized by the animal.3 Digestion plays a significant role in nutrient utilization. All the nutrients which are contained in the feed must be broken down into simpler forms so that the gut can absorb them for metabolic processes.

Introduction
Finishing broiler chickens at a reasonable period of time will guarantee profit maximization which is the target of poultry farmers. According to Ndelekwute et al. 1 provision of both quality and adequate feed to broiler chickens is essential for optimum performance. They maintained that quality feed is one which supplies all the nutrients required for productivity while adequate feeding means provision of the daily feed requirement free choice. It has been suggested that access to quality feed and attainment of daily requirement was better ways to achieve faster growth in broilers. 2 The quality and quantity of feed cannot guarantee optimum productivity if the feed so supplied is not well utilized by the animal. 3 Digestion plays a significant role in nutrient utilization. All the nutrients which are contained in the feed must be broken down into simpler forms so that the gut can absorb them for metabolic processes.
The gastro intestinal tract (GIT) otherwise known as the gut must be in a good condition in order to be able to carry out its responsibility of nutrient breakdown and absorption. 4 Hence the physiology and anatomy of the gut play a very significant role in carrying out this role. Also there are conditions which favour nutrient digestion and absorption by the gut. For instance low pH in the proventriculus is required for adequate protein digestion and general reduction in bacteria community in the different segments of the gut. 5 High pH in the gut results to proliferation pathogenic microbiota which is detrimental to both growth and health of chickens. 4 Presence of pathogenic bacteria results to nutrient fermentation which leads to high faecal moisture content and loss of vital nutrients. Nutrient fermentation in the gut can be aided by the viscosity of the digesta. 6 High digesta viscosity means that the digesta will stay in the duodenum longer than necessary thereby giving room for the pathogenic bacteria to ferment the digesta. The level of nutrient absorption depends among other factors on the condition of the absorptive machineries of the gut which are the villi. 6 Now that our chickens are produced in environments that are contaminated it becomes expedient to aid the natural gut to adequately perform its functions. Hence the gut can be modulated to achieve an improved performance of chickens. Feeding strategy to achieve this has been the use of feed grade enzymes, prebiotics, probiotics, spices, essential oils and recently organic acids. 7-11 Therefore, the objective of this research was to determine the effect of dietary organic acids (acetic, butyric, citric and formic acids) on nutrient digestibility, faecal moisture, digesta pH and viscosity of broiler chickens.

Site of experiment
The experiment was conducted at the Teaching and Research Farm of Department of Animal Nutrition and Forage Science, Michael Okpara University of Agriculture, Umudike, Nigeria.

Experimental design
Completely randomized design (CRD) was used. One hundred and fifty (150) day old Abor-acre chicks were used. There were divided into five treatments (T) groups which were each replicated three times with each replicate having 10 birds. Basal starter and finisher diets (Table 1) were formulate. The basal diets without any of the organic acids formed the control diets. Other treatments were formed by adding to the basal diets 0.25% acetic acid, butyric acid, citric acid and formic acid to represent T2-T5 respectively. The starter feed was fed for four weeks while the finisher feed was fed for three weeks. Water and feed were given ad libitum for the seven weeks the experiment lasted. day, vitamin and mineral preparation was added to their drinking water for seven days. Heat was supplied by using kerosene stove in order to maintained adequate temperature needed for the first three weeks. Feeding of the organic acids started from day old. Feed and water were supplied free choice. The birds were vaccinated against Newcastle and Gumboro diseases. The birds were fed starter diet for the first four weeks and finisher diet for the last three weeks.

Determination of faecal moisture and apparent nutrient digestibility
Total collection method was used using metabolism cages which were thoroughly washed and disinfected. At the end of the feeding experiment, one bird from each of the three replicates of a dietary group giving a total of 15 birds were randomly assigned to a metabolism cage each. Male birds were used and weight of the birds used were similar to reduce possible effect of sex and weight on digestibility. They were acclimatized for four days during which each treatment group was fed its diet. At the end of the acclimatization period, during which the birds had mastered the act of feeding and drinking in the new cage environment, a known quantity of the feed was given daily to each bird. To minimize feed wastages, feeding was done in the morning by 8.00 hours GMT in the afternoon by 1.00 hours and in the evening by 6.00 hours making sure the birds did not lack feed at any point in time. Each morning before feeding commenced, leftover feeds were recorded and feed intake noted.
Faeces was collected and weighed for four days. Collected faeces were immediately taken to the laboratory where they were oven dried at 60°C to constant weight. Dry faecal samples were ground to pass 1mm sieve. The four days faecal collection was pooled and thoroughly mixed together. A portion was taken from each treatment, stored in a refrigerator from which proximate analysis was carried out according to AOAC. 12 Faecal moisture and apparent nutrient digestibility was calculated thus; The percentage faecal moisture was determined by weighing a wet sample and the weight was noted. The wet sample was thereafter dried to a constant weight in an electric oven. The difference between the weight of the wet sample and the weight of the dry sample was taken to be the weight of the moisture. It was later converted to percentage by dividing the weight of the moisture by the weight of the wet sample and multiplied by 1.

Determination of viscosity and pH of digesta
Digesta obtained from the duodenum, ileum and caecum was used to measure the viscosity. Cone/plate geometry according to Lee et al. 6 was applied with cone angle 1o and diameter 40mm at 37°C. The digesta was suspended in 5mls distilled water. It was mechanically stirred for two hours at room temperature (30°C). The viscosity was measured using a Viscometer (Bohlin CS 50 Rheometer, manufactured by Bohlin Reologi, Muhlacker, Germany).
A pH meter was used to determine the pH. One gramme of the digeta was taken and mixed with 10mls of distilled deionized water. The pH was measured according to Nisbet et al. 13

Data transformation and statistical analysis
All the data obtained were subjected to analysis of variance (ANOVA). Significant means were separated using Duncan New Multiple Range Text (DNMRT) according to Steel & Torrie 14 Table 2 shows the effect of organic acid containing diets on faecal moisture and nutrient digestibility of broiler chickens. There was significant (P<0.05) reduction of faecal moisture by all the acidified diets. Addition of the organic acids had significant (P<0.05) effect on digestibility of all the nutrients except crude ash, nitrogen free extract and energy utilization. All the diets containing different organic acids significantly improved digestibility of dry matter, crude protein, crude fibre and ether extract. Inclusion of the organic acids in the diets negatively affected digestibility of nitrogen free extract. Though the organic acids did not improve digestibility of crude ash and energy utilization there were no negative effect on them as their respective values were similar to those of the control.

Results
The result of the effect of the diets on pH of the gastro intestinal tract (Table 3) indicates that feeding of diets containing organic acids resulted to significant (P<0.05) differences in digesta pH except in the crop and ileum where there was no dietary effect on pH. The pH was significantly reduced in proventriculus and duodenum by all the organic acid diets compared to the control. In the gizzard, acetic, citric and formic acid diets produced lower pH than the control diet while that of the butyric acid diet was similar to the control. All the organic acid diets produced lower pH than the control diet in the duodenum. The pH in caecum was significantly reduced by acetic and butyric acid diets compared to the control while that produced by citric and formic acid diets was similar to that of the control. The effect of treatment on digesta viscosity is also shown in Table  3 showed that significant difference (P<0.05) occurred only in the duodenum. Acetic and butyric acid diets significantly reduced the viscosity of the digesta in duodenum more than the control diet. In the same duodenum the digesta viscosity of dietary citric and formic acids was similar to that of the control. However, there were no differences in digesta viscosity within the organic acids.

Discussion
The ability of the organic acids to reduce the faecal moisture could be due to the significant improvement in fibre digestibility. Poor utilization of fibre has been reported to cause watery faeces in broiler chickens. 2,4 Undigested fibre in the gastro intestinal tract forms a substrate for bacteria and could be fermented by bacteria thereby generating water as one of the products. This culminates to high moisture content of faeces. Improving fibre digestibility means reduction in quantity of undigested fibre which would have been fermented by bacteria. The reduction in the faecal content moisture could lead to better litter quality and could be also a good indicator of healthy gut. Organic acids are known to induce pancreatic secretion and also could acidify intestinal digesta leading to reduction in gut pH. 5,15 The acidic nature of the proventriculus which is the stomach of chickens could be linked to the improvement in crude protein digestibility. According to Dibner 5 low pH is essential for protein digestion in chickens and other monogastric animals. Ndelekwute et al. 16 referring to ascorbic and citric acids content of lime juice reported that lime juice improved nutrient digestibility of 7 week old broiler chickens because of the two organic acids it contained.
Going by the present result which shows that the organic acids imparted negatively on the digestibility of nitrogen free extract, it could be opined that the acids could have played a major role. Nitrogen free extract is the starch portion of feeds and feedstuffs and amylase which is the enzyme that breaks down starch is negatively influenced in acidic medium. 17,18 This result is in consonant with the report of Ndelekwute et al. 3 that organic acids fed through drinking water did not improve digestibility of nitrogen free extract in chickens.
Digesta viscosity indicates the rate digesta flows or moves along the intestine and it has great impact on nutrient absorption and utilization in chickens. 10 The present result shows that acetic and butyric acids could be used to reduce digesta viscosity in the duodenum which is an essential part of the gut where digestion and most absorption of nutrients take place. The insignificant effect of the organic acids on digesta viscosity in the ileum and caecum could be that the acids have been diluted before getting to the hind gut and therefore could not impart on the digesta viscosity. Dilution of concentration of organic acids along the gastro intestinal tract has been reported. 15

Conclusion and recommendation
The four organic acids showed significant levels of positive effect on reduction of faecal moisture, acidification of the gut, digestibility of crude protein, crude fibre and ether extract. Among the organic acids, acetic and butyric acids indicated greater capacity to reduce the viscosity of digesta in the duodenum. Therefore application of the tested organic acids in feeding of broiler chickens is recommended to poultry farmers to reduce gut digesta pH, faecal moisture, improve nutrient digestibility and use acetic and butyric acids to reduce digesta viscosity in the duodenum.


extfieldsofstudy: Biology Medicine

text:
Resistance of House Fly, Musca domestica L. (Diptera: Muscidae), to Five Insecticides in Zhejiang Province, China: The Situation in 2017

Objectives High dependency on pesticides could cause selection pressure leading to the development of resistance. This study was conducted to assess the resistance of the house fly, Musca domestica, to five insecticides, namely, permethrin, deltamethrin, beta-cypermethrin, propoxur, and dichlorvos, in Zhejiang Province. Methods Field strains of house flies were collected from the 12 administrative districts in Zhejiang Province in 2011, 2014, and 2017, respectively. Topical application method was adopted for the bioassays. The probit analysis was used to determine the median lethal doses with the 95% confidence interval, and then the resistance ratio (RR) was calculated. The insecticides resistance in different years and the correlations of the resistance between different insecticides were also analyzed. Results The resistance of field strains house flies to insecticides in Zhejiang Province was relatively common, especially for permethrin, deltamethrin, and beta-cypermethrin. The reversion of the resistance to dichlorvos was found, and most of the field strains in Zhejiang Province became sensitive to dichlorvos in 2017. Propoxur was much easier to cause very high level of resistance; the Hangzhou strain had the highest RR value more than 1000 in 2014, and five field strains had the RR value more than 100 in 2017. Compared to 2011 and 2014, the resistance of the house flies to propoxur and deltamethrin increased significantly in 2017. The resistance of permethrin, deltamethrin, beta-cypermethrin, and propoxur was significantly correlated with each other, and the resistance of dichlorvos was significantly correlated with beta-cypermethrin. Conclusions Our results suggested that resistance was existed in permethrin, deltamethrin, beta-cypermethrin, and propoxur in the house flies of Zhejiang Province, while the resistance reversion to dichlorvos was found.

Introduction
e house fly, Musca domestica L. (Diptera: Muscidae), is one of the major public health pests responsible for the transmission of more than 100 pathogens of humans, poultry, and livestock [1][2][3]. rough contact with carcasses, excreta, garbage, and other septic matter, the house fly has close association with pathogens and plays a role in the mechanical transmission of pathogens (e.g., bacterial, protozoan, helminthic, and viral infections) to humans as well as domesticated animals [4]. Furthermore, diarrheal diseases and avian influenza transmitted by the house fly can cause human death, while high density of flies can reduce the aesthetic value of livestock products and usually brings economic losses [5,6].
Being fast acting, cheap and convenient, insecticides are often the preferred choice for controlling pests, including house flies. Globally, chemical insecticides such as pyrethroids, organophosphates, and carbamates have been used to control house flies [7]. Particularly in Zhejiang Province, some of the products such as dichlorvos, permethrin, deltamethrin, beta-cypermethrin, and propoxur have also been widely used. Although insecticide application is effective in reducing house flies density, high dependency on pesticides can cause selective pressure and further can lead to the development of resistance [4,8]. Due to the abuse of insecticides and cross resistance, even the efficiency of a new insecticide applied in pest controlling is limited [1,9]. Zhejiang is an eastern coastal province of China, dominated by subtropical monsoon climate, which is relatively moderate to the growth and reproduction of house flies. In recent years, avian influenza A (H 7 N 9 ) has broken out in Zhejiang Province [10]. Besides, along with infectious diseases including dengue fever, severe fever with thrombocytopenia syndrome et al., [11,12] and the national patriotic health campaign, a national public health movement in China aiming at the promotion of people's health, the use of a large amount of insecticides in vector control may cause the development of resistance. erefore, assessment of insecticide resistance risk was important in resistance management strategy application to maintain susceptibility in field strains of the house flies and to sustain the efficacy of these insecticides. e result might have public health implications to guide the use of insecticides and to delay the resistance development [5]. e aims of this study were to assess the resistance levels of the five insecticides of permethrin, deltamethrin, beta-cypermethrin, propoxur, and dichlorvos in house flies, to collect the baseline data for further monitoring and to guide the rational use of the insecticides in Zhejiang Province.

Insect Collection and Feeding.
e susceptible strain was originally introduced from the Chinese Center for Disease Control and Prevention (CDC) and reared for more than 30 generations in our laboratory without any insecticide exposure. Field strain house flies were collected from 12 administrative districts in Zhejiang Province, including Hangzhou, Ningbo, Wenzhou, Jiaxing, Shaoxing, Jinhua, Quzhou, Zhoushan, Lishui, Huzhou, Taizhou, and Yiwu. Within each district, 3∼5 sites were selected where the chemical insecticides were frequently used for the management of noisy or health-concerning insects including house flies, and each site more than 100 live house flies were collected by using sweep net. All the flies were maintained in controlled laboratory conditions [13].

Procedures of Bioassays.
e insecticides resistance risk was assessed three times in 2011, 2014 and 2017, respectively. All flies were tested at laboratory conditions with temperature maintained at 25 ± 1°C and relative humidity maintained at 60%∼80%. Topical application method used for the bioassays was from test methods of fIy resistance to insecticides, the bioassay methods for Musca domestica [14], which referred to the WHO Pesticide Evaluation Scheme [15]. e F1 generation female adult flies after 3∼5 days of eclosion which had the weight between 18 mg∼20 mg were tested. 30 female flies were tested per dose, and 5∼7 doses was designed for each insecticide. 1 μl of insecticide in acetone solution was applied on thoracic notum of the tested flies, and treated flies were transferred into plastic jars (250 ml) and reared with a cotton dental wick soaked with 20% sugar solution [9,14]. Each test was replicated three times, and flies treated with acetone alone acted as controls. Mortality counts were made 24 hours after the treatment.

Statistical Analysis.
e sensitive baseline method was used to detect the resistance. Probit analysis was used to determine the median lethal doses (LD 50 ) with the 95% confidence interval (CI) of the insecticides tested. Kruskal-Wallis H test was used to analyze the resistance to the insecticides in different years. Spearman rank correlation was used to analyze the correlation of the resistance between different insecticides. Among them, resistance ratio (RR) was calculated as follows [1]: RR � LD 50 of the field strain LD 50 of the susceptible reference strain .
(1) e resistant population was defined with the RR value ≥5 and no overlap between the 95% CIs of the tested strain and the susceptible strain [14]. For a high level of resistance, we combined the criterion of Shah et al., [16], defined as very low resistance (RR � 5-10), low resistance (RR � 11-20), moderate resistance (RR � 21-50), high resistance (RR � 51-100), and very high resistance (RR > 100). All the data were analyzed with SPSS16.0 software and a value of <0.05 was considered to be statistically significant.

General
Description. LD 50 and the RR value of different field strains to the five insecticides in Zhejiang Province are shown in Table 1. e RR value of the 11 prefecture-level cities in 2017 are shown in Figures 1 to 5. Yiwu, as a county, is affiliated to Jinhua, and the specific RR value was not shown on the map. Most field strains detected in Zhejiang Province were becoming relatively tolerant with dichlorvos in 2017, apart from the Huzhou and Wenzhou strain ( Figure 1). Although the resistance to Ningbo strain was not detected in 2017, tolerance to dichlorvos in the year 2011 and 2014 was seen. e RR value of the Hangzhou field strain to dichlorvos showed a decreasing trend from 15.3316 to 1.3638 in the past three detections (Table 1). e propoxur was extremely vulnerable to very high resistance. It was noteworthy that LD 50 of some field strains to the propoxur were too high to be accurately measured.
e RR value for the propoxur on Hangzhou strain was more than 1000 in 2014, the RR value of the Lishui strain and Taizhou strain was more than 700 in 2017, the RR value of the Zhoushan strain and Wenzhou strain was more than 300 in 2017, and the RR value of the Huzhou strain was 154.0477 in 2017. Although high levels of resistance were common, In general, the Ningbo strain had the lowest resistance level in 2011 and 2014, and the resistance was only found in deltamethrin with the RR value 5.8889 in 2014. e Jiaxing strain was still sensitive to certain insecticides such as dichlorvos, propoxur, and beta-cypermethrin in 2017, and the resistance insecticides were permethrin and deltamethrin with the RR value of 8.1296 and 10.5714, respectively. e Huzhou strain and Wenzhou strain had resistance to all the five insecticides in 2017, with very high-level resistance found in these two cities. e RR value of Wenzhou strain to propoxur, deltamethrin, and beta-cypermethrin was more than 300, and the RR value of Huzhou strain to propoxur and deltamethrin was 154.0477 and 157.1429, respectively.

Analysis of the Insecticides Resistance in Different Years.
e significant difference was found in the resistance to propoxur and deltamethrin, respectively, in the past three tests (P < 0.05) ( Table 2). Compared to 2011 and 2014, the resistance of the house flies to propoxur and deltamethrin increased notably in 2017 (P < 0.05).

Correlation of the Resistance between Different Insecticides.
e correlation of the resistance between different insecticides is shown in Table 3.
e RR value of betacypermethrin had correlation with dichlorvos, propoxur, permethrin, and deltamethrin, and the r s was 0.490, 0.504, 0.467, and 0.734, respectively (P < 0.05). Besides, the deltamethrin had correlation with permethrin and propoxur with the r s of 0.586 and 0.782, respectively, and permethrin had correlation with propoxur with the r s of 0.711 (P < 0.05).

Discussion and Conclusion
With emerging insecticides for the pest control, insecticides resistance management in house flies has become increasingly important. Based on the collected data in 2011, 2014, and 2017, we found that the resistance of the field strains house flies to five insecticides in Zhejiang Province was relatively spread, especially for three of them, namely, permethrin, deltamethrin, and beta-cypermethrin. e propoxur was easier to cause high level of resistance. e reversion of the resistance to dichlorvos was found. House flies' resistance to propoxur and deltamethrin increased notably in 2017. And the cross resistance might exist between certain insecticides.
According to the criterion, we found the resistance to permethrin, deltamethrin, and beta-cypermethrin was common in the field strains. All flies strains tested were resistant to permethrin and deltamethrin in 2017. Compared to 2011 and 2014, the resistance to deltamethrin had a significant rise and came up to a very high level in 2017. To beta-cypermethrin, although some field strains still appeared sensitive, very high-level resistance appeared in 2017. By contrast, the resistance level to permethrin was relatively low with all the RR values below 50 in the last three detections in Zhejiang Province from 2011 to 2017. A study conducted in Turkey showed that pyrethroids resistance changed from spring to fall in relation to usage and application frequencies of these insecticides [17]. e high level of resistance to betacypermethrin and deltamethrin in some field strains might be due to the extensive usage of these insecticides. Totally, pyrethroid usage was more common than other insecticides in Zhejiang Province in recent years. e wide application of pyrethroid might contribute to the rise of the house flies resistance.
Very high level of resistance in field populations was also found in propoxur, although some flies strains were still sensitive in Zhejiang Province. e RR value of propoxur in five strains was more than 100 in 2017, in which two strains were more than 700. One strain had the RR value more than 1000 in 2014, indicating the complete uselessness of the propoxur in house flies control. Very high level of resistance to propoxur had been reported before, and the RR value of the resistant strain to propoxur was found greater than 1189.48 compared with the susceptible strain by Chao et al. [18]. In field strains, recessive or dominant expression of the resistance depended on the type and dosage of pesticides used. Research had demonstrated that resistances inherited by a single gene developed more rapidly than those Canadian Journal of Infectious Diseases and Medical Microbiology controlled by two or more genes [19], while the house flies resistance to propoxur was inherited as a single, major, autosomal, and incompletely recessive factor [18], which might be the reason for the high level of resistance to propoxur.
Studies have found that house flies resistance to some insecticides was unstable and reversion might be found after several generations, but not to other insecticides [16,20,21]. Compared to the susceptible strain, some researchers found that certain insecticide resistance strains had a lower fecundity, hatchability, number of next-generation larvae, and net reproductive rate [16,20,22]. e resistance level might be decreased without the selection pressure of insecticides since the resistance alleles could be disadvantageous under natural selection [22]. In China, dichlorvos had been forbidden to use for controlling sanitary pest since 2008. As residual exposure, the reversion of the resistance in the house flies was obviously found, especially in Hangzhou field strain. Most of the field strains in Zhejiang Province became sensitive to dichlorvos in 2017, except the Huzhou and Wenzhou strains. e resistance maintained in this two field strains might be due to the fact that dichlorvos was still slightly used in controlling agriculturally insect pests. Besides, the cross resistance among dichlorvos and other insecticides might be another reason. In general, the resistance reversion to dichlorvos occurred in most of the field strains in Zhejiang Province over the past decade.
Cross resistance among the insecticides has raised concerns in the pesticide application [9,13]. e correlations among permethrin, deltamethrin, and beta-cypermethrin might indicate the cross resistance between these insecticides. e resistance and cross resistance of house flies between certain pyrethroid insecticides had been reported in previous studies [9,23]. e major explanation to pyrethroid resistance might be metabolic detoxification, decreased target site sensitivity, and decreased cuticular penetration [2]. e voltage-sensitive sodium channel mutations and increased detoxification mediated by cytochrome P450 monooxygenases overexpression might be the main reason for the resistance [4,8,22]. Sun et al. revealed that addition of T929I to the kdr mutation increased resistance to 13 different pyrethroids including permethrin, deltamethrin, and cypermethrin [24], which indicated that these pyrethroids might have cross resistance with each other. Besides, the correlation between propoxur and the pyrethroid insecticides should be noted, and the r s were 0.782, 0.711, and 0.504 with deltamethrin, permethrin, and beta-cypermethrin, respectively. e beta-cypermethrin had correlation  with dichlorvos with the r s 0.490. ese correlations might indicate the cross resistance or noncausal association between the insecticides, which need to be confirmed in further study. Because the cross resistance could affect the efficacy of the insecticides, rotating or mixing insecticides should not use the insecticides with cross resistance. e insecticides affected by different detoxification mechanisms should be incorporated together in resistance management strategies [5]. Besides, Khan et al., found insecticide mixtures could enhance the toxicity of insecticides, such as pyrethroids in the resistant population of house flies [25]. Because the resistance mechanism to each insecticide was independent and initially rare, it would decrease the chances for the occurrence of resistance to both insecticides at the same time [25]. Consequently, insecticide mixtures might be more effective in resistance management programs compared to mosaics or rotational use of these insecticides. Besides, comprehensive preventive and control measures involving chemical, biological, and environmental measures that exerted control over all the life stages of house flies should be used in pest management programs to minimize the selection pressure for resistance. Biological control of organisms was an alternative form of control of the house flies [26,27], but the resistance of the biorational insecticides should also be noted.
In Zhejiang Province, the resistance to insecticides varied in study districts, depending on which insecticides had been used and how often, how widely, and how long. e most frequently used insecticides showed the greatest ability to induce resistance in the target insects [28]. Totally, the Ningbo strain had the lowest resistance in the past two detections in 2011 and 2014. Besides, the Jiaxing strain was sensitive to three of the five insecticides detected in 2017, except for permethrin with the RR value of 8.1296 and deltamethrin with the RR value of 10.5714, which were relatively low resistance. e insecticides usage in Ningbo and Jiaxing might be more suitable in the management of resistance development. As the Huzhou and Wenzhou strains might have resistance problem to certain insecticides, this should be taken in account in future insecticides application.
As for the criteria, Shah et al. defined RR � 1 as the no resistant population and RR � 2-10 as the very low resistant population [16], and Khan et al. used the criteria that the insect populations with less than 10-fold RRs should be assumed as tolerant rather than resistant [9]; in our study, we defined the resistant population (RR ≥ 5 and the 95% CI did not overlap) according to e test methods of fly resistance to insecticides (GB/T 26350-2010) [15], combining the criterion of Shah et al. [16] to define the high level of resistance. Our standard might be more moderate in defining the resistance population. In our study, we paid more attention to the very high resistance region, because it might be difficult to reverse within a short time and in urgent need of the insecticide adjustment.   In conclusion, our findings confirmed the occurrence of resistance toward permethrin, deltamethrin, beta-cypermethrin, and propoxur in the house flies of Zhejiang Province, while the reversion of the resistance to dichlorvos was found. Continued use of such compounds insecticides would induce resistance in the target insects and influence their control effect. Integration of different insecticides with no cross resistance is a critical approach in integrated pest management strategies. Regular monitoring surveys should be continued to observe the level of the resistance and guide the insecticides usage in pest control. In further study, monitoring and identification of the resistant genes in the house flies will be essential for the management of resistance development.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
ere are no conflicts of interest.