Colour Feature Extraction Techniques of Fruits: A Survey
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paper_title: Applications of Image Processing in Agriculture : A Survey

paper_content:

Image processing has been proved to be effective tool for analysis in various fields and applications. Agriculture sector where the parameters like canopy, yield, quality of product were the important measures from the farmers’ point of view. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. The analysis of the parameters has proved to be accurate and less time consuming as compared to traditional methods. Application of image processing can improve decision making for vegetation measurement, irrigation, fruit sorting, etc.
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paper_title: Image analysis for agricultural processes: a review of potential opportunities

paper_content:

Abstract Image analysis and interpretation by computers has many potential applications for guidingor controlling agricultural processes. However, research from industrial applications cannot be applied directly to agriculture because of problems such as the biological variability of objects or workpieces, and difficulty in interpreting unstructured environments. This paper describes the process of image analysis, its application to agriculture, and thegeneric problems particularly relevant to agricultural processes. Priority areas for further research are suggested. Ways of including knowledge in algorithms should be studied. Specific applications should also be studied including the practical problems of integrating image processing into larger systems.
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paper_title: Development of a lemon sorting system based on color and size.

paper_content:

Grading systems give us many kinds of information such as size, color, shape, defect, and internal quality. Among these color and size are the most important features for accurate classification and/or sorting of citrus such as oranges, lemons and tangerines. Basically, two inspection stages of the system can be identified: external fruit inspection and internal fruit inspection. The former task is accomplished through processing of color images, while internal inspection requires special sensors for moisture, sugar and acid contents. In this paper, an efficient algorithm for grading lemon fruits is developed and implemented in visual basic environment. The system consists of two CCD cameras, two capture cards, an appropriate lighting system, a personal computer and other mechanical parts. The algorithm initially extracts the fruit from the background. The samples of different grades of lemon are situated in front of the cameras and are calibratted off-line. Then information on the HSI color values and estimated volumes of fruits are extracted and saved in a database. By comparing the information during sorting phase with the available information inside the database, the final grade of the passing fruits are determined. This algorithm can be easily adapted for grading and/or inspection of other agricultural products such as cucumber and eggplant.
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paper_title: A Fruit Size Detecting and Grading System Based on Image Processing

paper_content:

This paper presents a fruit size detecting and grading system based on image processing. After capturing the fruit side view image, some fruit characters is extracted by using detecting algorithms. According to these characters, grading is realized. Experiments show that this embedded grading system has the advantage of high accuracy of grading, high speed and low cost. It will have a good prospect of application in fruit quality detecting and grading areas.
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paper_title: Investigation and monitoring for leaves disease detection and evaluation using image processing

paper_content:

Image segmentation is useful in different applications. From the segmentation results, it is possible to identify regions of interest and objects in the scene, which is very beneficial to the subsequent image analysis. This system identifies leaves disease of plants and also determines the stage in which the disease is. The system has various image processing techniques. At first, the images are captured and processed for enhancement. Then image segmentation is carried out to get disease regions. Later, image features such as shape, colour and texture are extracted for the disease regions. These resultant features are given as input to disease classifier to appropriately identify and grade the diseases
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paper_title: A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)

paper_content:

This paper introduces a least-squares support vector machine (LS-SVM) classifier to detect the degree of browning on mango fruits as a function of fractal dimension (FD) and L^*a^*b^* values. Our results showed that the best classification accuracy of browning degree was up to 100% using the LS-SVM classifier based on FD and L^*a^*b^* (@c=6.13, @s^2=9.36). However, the correct classification rates of 85.19% and 88.89% were achieved for the LS-SVM models based on FD (@c=1.13, @s^2=5.52) and based on L^*a^*b^* (@c=6.68, @s^2=2.44), respectively. Therefore, this study indicated the possibility of developing a potentially useful classification tool using the LS-SVM combined with FD and L^*a^*b^* values for classifying the degree of browning on mango fruits during processing, storage and distribution.
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paper_title: Automated strawberry grading system based on image processing

paper_content:

Using machine-vision technology to grade strawberries can increase the commercial value of the strawberry. The automated strawberry grading system has been set up based on three characteristics: shape, size and colour. The system can efficiently obtain the shape characteristic by drawing the lines and then class with K-means clustering method for the strawberry image. The colour of the strawberry adopts the Dominant Colour method into the a* channel, and the size is described by the largest fruit diameter. The strawberry automated grading system can use one, two or three characteristics to grade the strawberry into three or four grades. In order to solve the multicharacteristic problems, the multi-attribute Decision Making Theory was adopted in this system. The system applied a conveyer belt, a camera, an image box, two photoelectrical sensors, a leading screw driven by a motor, a gripper, two limit switches and so on. The system was controlled by the single-chip-microcomputer (SCM) and a computer. The results show that the strawberry size detection error is not more than 5%, the colour grading accuracy is 88.8%, and the shape classification accuracy is above 90%. The average time to grade one strawberry is below 3s.
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paper_title: A computer vision approach for weeds identification through Support Vector Machines

paper_content:

This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.
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paper_title: Oil palm fruit bunch grading system using red, green and blue digital number

paper_content:

This research deals with the ripeness grading of oil palm fruit bunches. The current practice in the oil palm mills is to grade the oil palm bunches manually using human graders. This method is subjective and subject to disputes. In this research, we developed an automated grading system for oil palm bunches using the RGB color model. This grading system was developed to distinguish between the three different categories of oil palm fruit bunches. The maturity or color ripening index was based on different color intensity. Our grading system employs a computer and camera to analyze and interpret images equivalent to the human eye and brain. The colors namely Red, Green and Blue (RGB) of the palm oil fruit bunch were investigated using this grading system. The computer program developed and used the mean color intensity to differentiate between the different color and ripeness of the fruits such as oil palm FFB. The program results showed that the ripeness of fruit bunch could be differentiated between different categories of fruit bunches based on RGB intensity.
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paper_title: A real-time grading method of apples based on features extracted from defects

paper_content:

This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73%. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.
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paper_title: AE—Automation and Emerging Technologies: Weed Leaf Image Segmentation by Deformable Templates

paper_content:

Abstract In order to improve weeding strategies in terms of pesticide reduction, spatial distribution and characterization of in-field weed populations are important. With recent improvements in image processing, many studies have focused on weed detection by vision techniques. However, weed identification still remains difficult because of outdoor scenic complexity and morphological variability of plants. A new method of weed leaf segmentation based on the use of deformable templates is proposed. This approach has the advantage of applying a priori knowledge to the object searched, improving the robustness of the segmentation stage. The principle consists of fitting a parametric model to the leaf outlines in the image, by minimizing an energy term related to internal constraints of the model and salient features of the image, such as the colour of the plant. This method showed promising results for one weed species, green foxtail (Setaria viridis). In particular, it was possible to characterize partially occluded leaves. This constitutes a first step towards a recognition system, based on leaf characteristics and their relative spatial position.
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paper_title: Machine Vision System for Automatic Quality Grading of Fruit

paper_content:

Abstract Fruit and vegetables are normally presented to consumers in batches. The homogeneity and appearance of these have significant effect on consumer decision. For this reason, the presentation of agricultural produce is manipulated at various stages from the field to the final consumer and is generally oriented towards the cleaning of the product and sorting by homogeneous categories. The project ESPRIT 3, reference 9230 ‘Integrated system for handling, inspection and packing of fruit and vegetable (SHIVA)’ developed a robotic system for the automatic, non-destructive inspection and handling of fruit. The aim of this paper is to report on the machine vision techniques developed at the Instituto Valenciano de Investigaciones Agrarias for the on-line estimation of the quality of oranges, peaches and apples, and to evaluate the efficiency of these techniques regarding the following quality attributes: size, colour, stem location and detection of external blemishes. The segmentation procedure used, based on a Bayesian discriminant analysis, allowed fruits to be precisely distinguished from the background. Thus, determination of size was properly solved. The colours of the fruits estimated by the system were well correlated with the colorimetric index values that are currently used as standards. Good results were obtained in the location of the stem and the detection of blemishes. The classification system was tested on-line with apples obtaining a good performance when classifying the fruit in batches, and a repeatability in blemish detection and size estimation of 86 and 93% respectively. The precision and repeatability of the system, was found to be similar to those of manual grading.
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paper_title: Fish species classification by color, texture and multi-class support vector machine using computer vision

paper_content:

Remote diagnose of fish diseases for farmers is unrealized in China, but use of mobile phones and remote analysis based on image processing can be feasible due to the widespread use of mobile phones with camera features in rural areas. This paper presents a novel method of classifying species of fish based on color and texture features and using a multi-class support vector machine (MSVM). Fish images were acquired and sent by smartphone, and the method utilized was comprised of the following stages. Color and texture subimages of fish skin were obtained from original images. Color features, statistical texture features and wavelet-based texture features of the color and texture subimages were extracted, and six groups of feature vectors were composed. LIBSVM software was tested using leave-one-out cross validation to find the best group for classification in feature selection procedure. Two multi-class support vector machines based on a one-against-one algorithm were constructed for classification. The feature selection results showed that the Bior4.4 wavelet filter in HSV color space achieved greater accuracy than the other feature groups. The classification results indicate that only the DAGMSVM meets the requirement of time efficiency for the system. The results of this study suggest that the best classification model for fish species recognition is composed of a wavelet domain feature extractor with Bior4.4 wavelet filter in HSV color space and a one-against-one algorithm based DAGMSVM classifier.
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paper_title: Apple color grading based on organization feature parameters

paper_content:

This paper presents a system for apple color grading into four classes according to standards stipulated in China. To automatically grade apple fruit color, a laboratory machine vision system was developed, which consisted of a color CCD camera equipped with an image grab device, a bi-cone roller device controlled by a stepping motor, and a lighting source. Four images, one for every rotation of 90^o, were taken from each apple. Seventeen color feature parameters (FP) were extracted from each apple in the image processing. Three hundred and eighteen ''Fuji'' apples were examined by the system, and were divided into two sets, with 200 in ''Training set'' and 118 in ''Test set''. A method called organization feature parameter (OFP), based on formulae expression trees by using genetic algorithms (GA), was used in this paper. When the initial FP could not sensitively distinguish among different classes of apples, the FP were organized into one new OFP by using genetic algorithm. By applying the step decision tree algorithm in combination with the OFP method, high grade judgment ratios were achieved in the classification of two of four apple color grades, i.e., 'Extra', and 'Reject'. However, the grade judgment ratio for 'class I' and 'class II' was relatively low. Compared with BP-ANN and SVM, the OFPs method was more accurate than BP-ANN, but a little lower than SVM for identification results.
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paper_title: New Approaches for Size Determination of Apple Fruits for Automatic Sorting and Grading

paper_content:

Nondestructive quality evaluation of fruits is important and very vital for the food and agricultural industry. The fruits in the market should satisfy the consumer preferences. Traditionally grading of fruits is performed primarily by visual inspection using size as a particular quality attribute. Image processing offers solution for automated fruit size grading to provide accurate, reliable, consistent and quantitative information apart from handling large volumes, which may not be achieved by employing human graders. A fruit grading system based on machine vision technology to assess the fruits quality would bring economical benefits to the fruit industry by increasing consumer/buyer confidence in the quality of the fruit. Conventional methods of size determination are not suitable for objects of irregular shape. Majority of the apples have good degree of symmetry about the stem and calyx axis. The paper discusses three different methods to determine the size of this category of apples by applying the known geometrical models. Three more methods are presented in the paper to determine the size of the other category of apples. The Apple fruit images in different postures are captured to cover the entire fruit, using a CCD camera. The analysis is carried out using MATLAB ® package on Windows ® platform. It was found that though all the methods outlined in the paper could be used, some methods achieve higher performance. RUITS and vegetables are graded based on their external factors like size, shape, color, external damage, etc. Classification of fruits based on size is one of the most important tasks performed in the packinghouses. Machine vision technology has been proved to be an excellent tool that could be effectively used to replace human sorters for consistent and reliable judgment in estimating and comparing sizes of the fruits. Many methods like area measurement, linear regression relations between the areas taken from two different points of view, comparing measurement of length, width, perimeter etc., may be employed to estimate and compare fruit sizes. Ratio of length of major and minor axes, contour of random views of the fruits can be used for size comparison. Fruits, vegetables, and cereals size determination using
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paper_title: Development of a lemon sorting system based on color and size.

paper_content:

Grading systems give us many kinds of information such as size, color, shape, defect, and internal quality. Among these color and size are the most important features for accurate classification and/or sorting of citrus such as oranges, lemons and tangerines. Basically, two inspection stages of the system can be identified: external fruit inspection and internal fruit inspection. The former task is accomplished through processing of color images, while internal inspection requires special sensors for moisture, sugar and acid contents. In this paper, an efficient algorithm for grading lemon fruits is developed and implemented in visual basic environment. The system consists of two CCD cameras, two capture cards, an appropriate lighting system, a personal computer and other mechanical parts. The algorithm initially extracts the fruit from the background. The samples of different grades of lemon are situated in front of the cameras and are calibratted off-line. Then information on the HSI color values and estimated volumes of fruits are extracted and saved in a database. By comparing the information during sorting phase with the available information inside the database, the final grade of the passing fruits are determined. This algorithm can be easily adapted for grading and/or inspection of other agricultural products such as cucumber and eggplant.
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paper_title: Citrus sorting by identification of the most common defects using multispectral computer vision

paper_content:

Abstract The presence of skin defects is one of the most influential factors in the price of fruit. The detection of defects during packing operations ensures that only fruits with a good quality reach the market. Moreover, the identification of the type of each defect will increase both the quality of the fruit and also the producer’s profit. At the present time, fruit with slight defects is marketed together with sound fruit, thus depreciating the quality of the batch, or it is removed together with seriously damaged fruit, thereby causing economic losses. Most current computer vision systems used in the automatic quality inspection of food are limited to the visible region of the electromagnetic spectrum as they tend to imitate the human eye. However, non-visible information, such as that provided by near-infrared or ultraviolet regions of the spectrum, can improve the inspection by detecting specific defects or allowing the detection of non-visible damages. This work summarises our research in the application of near-infrared, ultraviolet and fluorescence computer vision systems in the identification of the most common defects of citrus fruits, and proposes a fruit sorting algorithm that combines this different spectral information (including visible) to classify fruit according to the type of defect. Results showed that the contribution of non-visible information can improve the detection and identification of some defects. Compared with the results from colour images, the detection accuracy of anthracnose increased from 86% by using NIR images; and the accuracy of green mould was increased from 65% to 94% by using images of fluorescence.
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paper_title: Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system

paper_content:

Software for detecting the quality features of golden delicious starfruits of Averrhoa carambola L. genus were developed for automated inspection system using machine vision technology. The features considered were colour and shape. The use of artificial classifiers such as linear discrimination analysis and multi-layer perceptron neural network as a tool to detect starfruit maturities such as unripe, underripe, ripe and overripe in HSI colour space were investigated. The colour spectra of matured and unmatured fruits were characterised using all colour features ranging from hue 10 to hue 74, and, using principal hues generated by Wilks-lambda analysis. Experiments performed on 200 independent starfruit samples revealed that linear discriminant analysis after Wilks-lambda analysis was more precise in classification than direct application of linear discriminant analysis. However, the classification accuracy of multi-layer preceptron remained relatively the same before and after feature reduction. Overall, the average correct classification for linear discriminant analysis and multi-layer preceptron was 95.3% and 90.5% respectively during testing stage. Meanwhile the use of Fourier transform was investigated for shape discrimination. This algorithm produced 100% success rate in detecting starfruits by three shape categories: well-formed, slightly deformed and seriously deformed. Both colour and shape analysis was easily affected by the variation of lighting levels, and this contributed to the major classification error.
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paper_title: Adaptive texture and color segmentation for tracking moving objects

paper_content:

Color segmentation is a very popular technique for real-time object tracking. However, even with adaptive color segmentation schemes, under varying environmental conditions in video sequences, the tracking tends to be unreliable. To overcome this problem, many multiple cue fusion techniques have been suggested. One of the cues that complements color nicely is texture. However, texture segmentation has not been used for object tracking mainly because of the computational complexity of texture segmentation. This paper presents a formulation for fusing texture and color in a manner that makes the segmentation reliable while keeping the computational cost low, with the goal of real-time target tracking. An autobinomial Gibbs Markov random field is used for modeling the texture and a 2D Gaussian distribution is used for modeling the color. This allows a probabilistic fusion of the texture and color cues and for adapting both the texture and color over time for target tracking. Experiments with both static images and dynamic image sequences establish the feasibility of the proposed approach.
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paper_title: An image-processing based algorithm to automatically identify plant disease visual symptoms

paper_content:

This study describes an image-processing based method that identifies the visual symptoms of plant diseases, from an analysis of coloured images. The processing algorithm developed starts by converting the RGB image of the diseased plant or leaf, into the H, I3a and I3b colour transformations. The I3a and I3b transformations are developed from a modification of the original I1I2I3 colour transformation to meet the requirements of the plant disease data set. The transformed image is then segmented by analysing the distribution of intensities in a histogram. Rather than using the traditional approach of selecting the local minimum as the threshold cut-off, the set of local maximums are located and the threshold cut-off value is determined according to their position in the histogram. This technique is particularly useful when the target in the image data set is one with a large distribution of intensities. In tests, once the image was segmented, the extracted region was post-processed to remove pixel regions not considered part of the target region. This procedure was accomplished by analysing the neighbourhood of each pixel and the gradient of change between them. To test the accuracy of the algorithm, manually segmented images were compared with those segmented automatically. Results showed that the developed algorithm was able to identify a diseased region even when that region was represented by a wide range of intensities.
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paper_title: Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) Using Neuro-fuzzy Technique

paper_content:

Automated fruit grading in local fruit industries are gradually receiving attention as the use of technology in upgrading the quality of food products are now acknowledged. In this paper, outer surface colors of palm oil fresh fruit bunches (FFB) are analyzed to automatically grade the fruits into over ripe, ripe and unripe. We compared two methods of color grading: 1) using RGB digital numbers and 2) colors classifications trained using a supervised learning Hebb technique and graded using fuzzy logic. A total of 90 images are used as the training images and 45 images are tested in the grading process. Overall, automated grading using RGB digital numbers produced an average of 49% success rate, while the neuro-fuzzy approach achieved an accuracy level of 73.3%.
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paper_title: On-Line Detection of Defects on Fruit by Machinevision Systems Based on Three-Color-Cameras Systems

paper_content:

How to identify apple stem-ends and calyxes from defects is still a challenging project due to the complexity of the process. It is know that the stem-ends and calyxes could not appear at the same image. Therefore, a contaminated apple distinguishing method is developed in this article. That is, if there are two or more doubtful blobs on an apple´s image, the apple is contaminated one. There is no complex imaging process and pattern recognition in this method, because it is only need to find how many blobs (including the stem-ends and calyxes) in an apple´s image. Machine vision systems which based 3 color cameras are presented in this article regarding the online detection of external defects. On this system, the fruits placed on rollers are rotating while moving, and each camera which placed on the line grabs 3 images from an apple. After the apple segmented from the black background by multi-thresholds method, defect´s segmentation and counting is performed on the apple´s images. Good separation between normal and contaminated apples was obtained for threecamera system (94.5%), comparing to one-camera system (63.3%), twocamera system (83.7%). The disadvantage of this method is that it could not distinguish different defects types. Defects of apples, such as bruising, scab, fungal growth, and disease, are treated as the same.
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paper_title: Artificial vision to assure coffee-Excelso beans quality

paper_content:

This paper studies the possibility of classifying coffee beans by using their features of color, shape and size. The process of acquiring the images were done in controlled lighting conditions. Based on segmented images, color, shape and size provide information about the physical alteration in green coffee beans, during the growing and drying process that affect the flavor and taste of the drink by Developing a computer program to sort coffee beans by types In the case of color images are analyzed in RGB space, which were preprocessor in order to reduce noise and separate from the background and enhance its features. The location was used for an algorithm for identifying and setting contours ellipse, an algorithm Mahalanobis distance classifier, an algorithm called Flood of growth, for the segmentation of those defective grains. The emergence of methods that take advantage of technological advances is an option whose benefits encourage the study of new possibilities. The classification of coffee beans throughout the analysis of images is a very promising because it is a minimally invasive method and the exposure of the grains are not visible to look deteriorates significantly. Many farmers sort their coffee beans by hand but only a few thousand dollars invested in a automatic sorting optical machine such us Sortex or Xeltron. In this paper we develop algorithms based on image processing techniques for the classification of defective beans
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paper_title: Region-based image retrieval using integrated color, shape, and location index

paper_content:

A technique to retrieve images by region matching using a combined feature index based on color, shape, and location is presented within the framework of MPEG-7. Dominant regions within each image are indexed using integrated color, shape, and location features. Various combinations of regions are also indexed. The resulting indices and related metadata are stored in a Hash structure, where similar images tend to form clusters. The retrieval process is non-cascading and images can be retrieved based on color, shape or location and also based on a combined color shape location index. Results obtained show that retrieval effectiveness increases in non-cascaded region-based querying by combined index.
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paper_title: Machine vision based quality evaluation of Iyokan orange fruit using neural networks

paper_content:

It is a common belief that a sweet Iyokan orange fruit is reddish in color, of medium size, with a height to width ratio less than one, and having a glossy surface. However, the criteria are ambiguous and vary from people to people and locations to locations. In this paper, sugar content and acid content of Iyokan orange fruit were evaluated using a machine vision system. Images of 30 Iyokan orange fruits were acquired by a color TV camera. Features representing fruit color, shape, and roughness of fruit surface were extracted from the images. The features included R/G color component ratio, Feret's diameter ratio, and textural features. These features and weight of the fruit were entered to the input layers of neural networks, while sugar content or pH of the fruit was used as the values of the output layers. Several neural networks were found to be able to predict the sugar content or pH from the fruit appearance with a reasonable accuracy.
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paper_title: Automatic fruit and vegetable classification from images

paper_content:

Contemporary Vision and Pattern Recognition problems such as face recognition, fingerprinting identification, image categorization, and DNA sequencing often have an arbitrarily large number of classes and properties to consider. To deal with such complex problems using just one feature descriptor is a difficult task and feature fusion may become mandatory. Although normal feature fusion is quite effective for some problems, it can yield unexpected classification results when the different features are not properly normalized and preprocessed. Besides it has the drawback of increasing the dimensionality which might require more training data. To cope with these problems, this paper introduces a unified approach that can combine many features and classifiers that requires less training and is more adequate to some problems than a naive method, where all features are simply concatenated and fed independently to each classification algorithm. Besides that, the presented technique is amenable to continuous learning, both when refining a learned model and also when adding new classes to be discriminated. The introduced fusion approach is validated using a multi-class fruit-and-vegetable categorization task in a semi-controlled environment, such as a distribution center or the supermarket cashier. The results show that the solution is able to reduce the classification error in up to 15 percentage points with respect to the baseline.
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paper_title: Estimation of mango crop yield using image analysis - Segmentation method

paper_content:

This paper presents an approach to count mango fruit from daytime images of individual trees for the purpose of a machine vision based estimation of mango crop yield. Images of mango trees were acquired over a three day period, 3weeks before commercial harvest occurred. The fruit load of each of fifteen trees was manually counted, and these trees were imaged on four sides. Correlation between tree counts and manual image counts was strong (R^2=0.91 for two sides). A further 555 trees were imaged on one side only. For these images, pixels were segmented into fruit and background pixels using colour segmentation in the RGB and YCbCr colour ranges and a texture segmentation based on adjacent pixel variability. Resultant blobs were counted to obtain a per image mango count. Across a set of 555 images (with mean+/-standard deviation of fruit per tree of 32.3+/-14.3), a linear regression, (y=0.582x-0.20, R^2=0.74, bias adjusted root mean square error of prediction=7.7) was achieved on the machine vision count relative to the image count. The algorithm decreased in effectiveness as the number of fruit on the tree increased, and when imaging conditions involved direct sunlight. Approaches to reduce the impact of fruit load and lighting conditions are discussed.
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paper_title: Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield

paper_content:

New apple fruit recognition algorithms based on colour features are presented to estimate the number of fruits and develop models for early prediction of apple yield, in a multi-disciplinary approach linking computer science with agricultural engineering and horticulture as part of precision agriculture. Fifty cv. ‘Gala’ apple digital images were captured twice, i.e. after June drop and during ripening, on the preferred western side of the tree row with a variability of between 70 and 170 fruit per tree, under natural daylight conditions at Bonn, Germany. Several image processing algorithms and fruit counting algorithms were used to analyse the apple images. Finally, an apple recognition algorithm with colour difference R − B (red minus blue) and G − R (green minus red) was developed for apple images after June drop, and two different colour models were used to segment ripening period apple images. The algorithm was tested on 50 images of trees in each period. Close correlation coefficients R 2 of 0.80 and 0.85 were obtained for two developmental periods between apples detected by the fruit counting algorithm and those manually counted. Two sets of data in each period were used for modelling yield prediction of the apple fruits. In the calibration data set, the R 2 values between apples detected by the fruit counting algorithm and actual harvested yield were from 0.57 for young fruit after June drop to 0.70 in the fruit ripening period. In the validation data set, the R 2 value between the number of apples predicted by the model and actual yield at harvest ranged from 0.58 to 0.71. The proposed model showed great potential for early prediction of yield for individual trees of apple and possibly other fruit crops.
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paper_title: A practical solution for ripe tomato recognition and localisation

paper_content:

This article presents straightforward and fast algorithms that achieve low-cost computation of a software/hardware process for a practical robot vision solution for ripe tomato picking in greenhouses. The solution has three steps. First, it uses a single-colour component V in a YUV colour space to segment out the ROI (region of interest) of ripe tomatoes from the background, green stems and foliage. Second, an edge extractor with a constrained curvature is developed to extract the arc edges within a certain radius range based on the ripe tomato model for calculating the central points of each ripe tomato that was detected in the first step. Finally, according to the circle---circle relationship found in the geometric analysis, the overlapping area between two ripe tomatoes is calculated, and the depth ordering of the two overlapping ripe tomatoes is determined. The key contribution of this proposed solution is the real-time recognition and localisation of ripe tomatoes that are covered by green foliage, stems and unripe tomatoes in the real world. Simplicity and practicality are the key considerations of this solution.
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paper_title: Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping

paper_content:

Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept.
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paper_title: Machine vision based quality evaluation of Iyokan orange fruit using neural networks

paper_content:

It is a common belief that a sweet Iyokan orange fruit is reddish in color, of medium size, with a height to width ratio less than one, and having a glossy surface. However, the criteria are ambiguous and vary from people to people and locations to locations. In this paper, sugar content and acid content of Iyokan orange fruit were evaluated using a machine vision system. Images of 30 Iyokan orange fruits were acquired by a color TV camera. Features representing fruit color, shape, and roughness of fruit surface were extracted from the images. The features included R/G color component ratio, Feret's diameter ratio, and textural features. These features and weight of the fruit were entered to the input layers of neural networks, while sugar content or pH of the fruit was used as the values of the output layers. Several neural networks were found to be able to predict the sugar content or pH from the fruit appearance with a reasonable accuracy.
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Title: Colour Feature Extraction Techniques of Fruits: A Survey
Section 1: INTRODUCTION
Description 1: This section introduces the significance of agriculture in the Indian economy and the relevance of image processing technology in fruit grading and sorting processes.

Section 2: OBJECTIVE OF OUR STUDY
Description 2: This section outlines the objective of developing an automated grading system for mangoes and the importance of studying colour feature extraction techniques.

Section 3: COLOUR FEATURE EXTRACTION TECHNIQUES
Description 3: This section explores various techniques used for colour feature extraction in fruit grading, detailing each method and their applications.

Section 4: Dominant Colour Method
Description 4: This subsection explains the Dominant Colour Method used in strawberry grading and its implementation steps.

Section 5: Region based approaches
Description 5: This subsection discusses the region-based approaches that group pixels into homogeneous regions for image segmentation.

Section 6: Edge detection approaches
Description 6: This subsection describes edge detection techniques that focus on locating points of significant changes in gray level within images.

Section 7: Fuzzy approaches
Description 7: This subsection explores the use of fuzzy logic, operators, and inference rules in colour feature extraction.

Section 8: Neural network approaches
Description 8: This subsection details the application of neural networks for classification and clustering in colour feature extraction.

Section 9: Intensity Distribution Method
Description 9: This subsection discusses how color intensity distribution is used in the grading of dates based on their flabbiness.

Section 10: Mean of colour in images
Description 10: This subsection examines the use of mean colour values in images for grading various fruits, including mangoes and apples.

Section 11: Nine Colour Characteristic Data
Description 11: This subsection details the method of extracting nine characteristic data points from apple images for colour grading.

Section 12: HSI Colour Model Technique
Description 12: This subsection explains the use of the HSI colour model in lemon and apple sorting based on color parameters.

Section 13: YES Colour Model Technique
Description 13: This subsection describes the application of the YES colour model for adaptive texture and colour segmentation.

Section 14: Different HSI and I1I2I3
Description 14: This subsection discusses the use of HSI and I1I2I3 colour models in identifying plant diseases, which can be applied to fruit colour feature extraction.

Section 15: Simple RGB model
Description 15: This subsection explains the direct use of the RGB model in quality assurance and defect detection of fruits.

Section 16: The Segmentation Method
Description 16: This subsection explores different segmentation techniques using colour spaces like YUV, for ripening and yield estimation of fruits like tomatoes and mangoes.

Section 17: Direct Colour Mapping Technique
Description 17: This subsection describes the direct colour mapping method that converts RGB values to colour indices for rapid quality grading.

Section 18: CONCLUSION AND FUTURE DIRECTION
Description 18: This section concludes the survey, summarizing the importance of colour feature extraction techniques in fruit grading and suggesting future directions for research and development.