Applications of Image Processing in Agriculture : A Survey
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paper_title: Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

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Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of time and resources for ground-truthing. An additional challenge in mapping irrigation across large areas occurs in fragmented landscapes with small irrigated and cultivated fields, where the spatial scale of observations is pitted against the need for high frequency temporal acquisitions. Finally, this review identifies passive and active microwave observations, advanced image classification methods, and data fusion including optical and radar sensors or with information from sources with multiple spatial and temporal characteristics as key areas where additional research is needed.
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paper_title: Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE).

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Extracting endmembers from remotely-sensed images of vegetated areas can present difficulties. In this research, we applied a recently-developed endmember-extraction algorithm based on Support Vector Machines to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of SVM-BEE, N-FINDR, and SMACC algorithms in extracting endmembers from a real, predominantly-agricultural scene. SVM-BEE estimated vegetation and other endmembers for all classes in the image, which N-FINDR and SMACC failed to do. SVM-BEE was consistent in the endmembers that it estimated across replicate trials. Spectral angle mapper (SAM) classifications based on SVM-BEE-estimated endmembers were significantly more accurate compared with those based on N-FINDR- and (in general) SMACC-endmembers. Linear spectral unmixing accrued overall accuracies similar to those of SAM.
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paper_title: Remote sensing imagery in vegetation mapping : a review

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Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.
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paper_title: Classification of vitreousness in durum wheat using soft X-rays and transmitted light images

paper_content:

Abstract Hardness is a kernel characteristic that influences both milling and processing characteristics of wheat. It is one characteristic that is used for segregating wheat to meet the needs for various products. Kernel virteousness is a visual marker for hardness and is the characteristic assessed during the grading process. The potential of classifying vitreous and non-vitreous durum wheat kernels, in crease-down position, using imaging systems based on real time soft X-rays or transmitted light was assessed in this study. Durum wheat kernels at 14, 15 and 16% moisture contents were used as samples in this study. Image features modeling gray level distribution, textural and shape moments were measured and used to develop a classification system for vitreous and non-vitreous durum wheat kernels. The classification accuracies were 76% for vitreous kernels and 82% for non-vitreous kernels at 16% moisture content using the soft X-ray system but for the transmitted light system, the classification accuracies were 86% for vitreous and 93% for non-vitreous kernels. Moisture content had no effect on classifying vitreous or non-vitreous kernels by the transmitted light system but the classification accuracies increased with moisture content for non-vitreous kernels by the soft X-ray system.
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paper_title: X-ray detection of defects and contaminants in the food industry

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The ability of X-rays to traverse through matter and reveal hidden contaminants or defects has led to their extensive use in manufacturing industries for quality control inspection. The difficulties inherent in the detection of defects and contaminants in food products have kept the use of X-ray in that industry limited mainly to the packaged foods sector. Nevertheless, the need for non-destructive internal product inspection has motivated a considerable research effort in this field spanning many decades. Improvements in technology, especially more compact and affordable high voltage power sources, high speed computing, and high resolution detector arrays, have made many X-ray detection tasks possible today that were previously unfeasible. These improvements can be expected to continue into the future. The purpose of this article is to give a review of research activity related to the use of X-ray imaging for the detection of defects and contaminants in agricultural commodities and discuss improvements in technology required to improve these detection capabilities.
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paper_title: Improving weed pressure assessment using digital images from an experience-based reasoning approach

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One of the main goals of Precision Agriculture is site-specific crop management to reduce the production of herbicide residues. This paper presents a computer-based image analysis system allowing users to input digital images of a crop field, and to process these by a series of methods to enable the percentages of weeds, crop and soil present in the image to be estimated. The system includes a Case-Based Reasoning (CBR) system that, automatically and in real time, determines which processing method is the best for each image. The main challenge in terms of image analysis is achieving appropriate discrimination between weeds, crop and soil in outdoor field images under varying light, soil background texture and crop damage conditions. The performance of the developed system is shown for a set of images acquired from different fields and under different, uncontrolled conditions, such as different light, crop growth stage and size of weeds, reaching correlation coefficients with real data of almost 80%.
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paper_title: Wavelet transform to discriminate between crop and weed in perspective agronomic images

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We proposed testing and validating the accuracy of four image processing algorithms (wavelet transforms and Gabor filtering) for crop/weed discrimination in synthetic and real images. A large panel of wavelet bases (33) was tested and the two best wavelets and the worst one were selected for detailed study. Based on a confusion matrix the crop/weed classification results of wavelet transforms were compared to the results of Gabor filtering that was initially chosen to develop a machine vision system for a real-time precision sprayer. The accuracy of these algorithms was compared and showed that wavelets were well adapted for perspective images: the best results were with Daubechies 25 and discrete approximation Meyer wavelets. They provided better results than Gabor filtering not only for crop/weed classification but also in processing time.
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paper_title: Weed Recognition Based on Erosion and Dilation Segmentation Algorithm

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Farmer needs alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds and there are no selective herbicides for some weed situation. Since hand labor is costly, an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Many attempts have been made to develop efficient algorithms for recognition and classification. Currently research is going on for developing new machine vision algorithms for automatic recognition and classification of many divers object groups. In this paper an algorithm is developed for automatic spray control system. The algorithm is based on erosion followed by dilation segmentation algorithm. This algorithm can detect weeds and also classify it. Currently the algorithm is tested on two types of weeds i.e. broad and narrow. The developed algorithm has been tested on these two types of weeds in the lab, which gives a very reliable performance. The algorithm is applied on 240 images stored in a database in the lab, of which 100 images were taken from broad leaf weeds and 100 were taken from narrow leaf weeds, and the remaining 40 were taken from no or little weeds. The result showed over 89% results
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paper_title: Edge based Real-Time Weed Recognition System for Selective Herbicides

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The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm which is based on edge based weed classifier is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 94 % classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.
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paper_title: Tomato quality evaluation with image processing: A review

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th May, 2011 Tomatoes are in high demand because the world population consumes them daily. This research aims to improve tomato production and fruit quality through fruit measurement methods, which have a low impact factor on the fruit and plant during measurements. In the present paper, we present a review of the main attributes, such as color, ripening rate, firmness, shape, size and composition, that determine tomato fruit quality for final consumers; we also overview the methods (invasive destructive and invasive nondestructive) currently used to evaluate these attributes. The future trend in attribute analysis involves the development of portable, low-cost devices that take images directly from crops in the field to instantly determine quality characteristics.
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paper_title: Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach

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One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T^2 statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection.
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paper_title: Combining discriminant analysis and neural networks for corn variety identification

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Variety identification is an indispensable tool to assure grain purity and quality. Based on machine vision and pattern recognition, five China corn varieties were identified according to their external features. Images of non-touching corn kernels were acquired using a flat scanner. A total of 17 geometric features, 13 shape and 28 color features were extracted from color images of corn kernels. Two optimal feature sets were generated by stepwise discriminant analysis, and used as inputs to classifiers. A two-stage classifier combining distance discriminant and a back propagation neural network (BPNN) was built for identification. On the first stage, corn kernels were divided into three types: white, yellow and mixed corn by distance discriminant analysis. And then different varieties in the same type were identified by an improved BPNN classifier. The classification accuracies of BAINUO 6, NONGDA 86, NONGDA 108, GAOYOU 115, and NONGDA 4967 were 100, 94, 92, 88 and 100%, respectively.
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paper_title: PROSPECTS OF COMPUTER VISION AUTOMATED SORTING SYSTEMS IN AGRICULTURAL PROCESS OPERATIONS IN NIGERIA

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The paper presents the recent development and application of image analysis and computer ::: machine vision in sorting of agricultural materials and products in the food industries. Basic ::: concepts and technologies associated with computer vision, a tool used in image analysis and ::: automated sorting are highlighted. ::: For the ever-increasing population, losses in handling and processing and the increased ::: expectation of food products of high quality and safety standards, there is need for the growth of ::: accurate, fast and objective quality determination of the characteristics of food and agricultural ::: products. Computer vision and image analysis, are non-destructive and cost-effective technique ::: for sorting agricultural products during handling processes and commercial purposes. Different ::: approaches based on image analysis and processing identified are related to variety of ::: applications in food and agricultural products. ::: Suggestions were made on the ways of adapting the growing trends to agricultural and food ::: industries in Nigeria.
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paper_title: Color image based sorter for separating red and white wheat

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A simple imaging system was developed to inspect and sort wheat samples and other grains at mod- erate feed-rates (30 kernels/s or 3.5 kg wheat/h). A single camera captured color images of three sides of each kernel by using mirrors, and the images were processed using a personal computer (PC). Real time image acquisition and processing was enabled on an ordinary PC under Windows XP operating system using the IEEE 1394 data transfer protocol, DirectX application software, and dual-core computer processor. Image acquisition and transfer to the PC required approximately 17 ms per kernel, and an additional 1.5 ms was required for image processing. After classification, the computer could output a signal from the parallel port to activate an air valve to divert (sort) kernels into a secondary container. Hard red and hard white wheat kernels were used in this study to test and demonstrate sorter capability. Simple image statistics and histograms were used as features. Discriminant analysis was per- formed with one, two, or three features to demonstrate classification improvements with increased numbers of features. The sorter was able to separate hard red kernels from hard white kernels with 95 to 99% accuracy, depending on the wheat varieties, feed-rate, and number of classification features. The system is an economical and useful instrument for sorting wheat and other grains with high accuracy.
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paper_title: Hardware-based image processing for high-speed inspection of grains

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A high-speed, low-cost, image-based sorting device was developed to detect and separate grains having slight color differences or small defects. The device directly combines a complementary metal-oxide-semiconductor (CMOS) color image sensor with a field-programmable gate array (FPGA) which was programmed to execute image processing in real-time, without the need of an external computer. Spatial resolution of the imaging system is approximately 16pixels/mm. The system includes three image sensor/FPGA combinations placed around the perimeter of a single-file stream of kernels, so that most of the surface of each kernel is be inspected. A vibratory feeder feeds kernels onto an inclined chute that kernels slide down in a single-file manner. Kernels are imaged immediately after dropping off the end of the chute and are diverted by activating an air valve. The system has a throughput rate of approximately 75kernels/s per channel which is much higher than previously developed image inspection systems. This throughput rate corresponds to an inspection rate of approximately 8kg/h of wheat and 40kg/h of popcorn. The system was initially developed to separate white wheat from red wheat, and to remove popcorn having blue-eye damage, which is indicated by a small blue discoloration in the germ of a popcorn kernel. Testing of the system resulted in accuracies of 88% for red wheat and 91% for white wheat. For popcorn, the system achieved 74% accuracy when removing popcorn with blue-eye damage and 91% accuracy at recognizing good popcorn. The sorter should find uses for removing other defects found in grain, such as insect-damaged grain, scab-damaged wheat, and bunted wheat. Parts for the system cost less than $2000, suggesting that it may be economical to run several systems in parallel to keep up with processing plant rates.
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paper_title: In-line detection of apple defects using three color cameras system

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Identification of apple stem-ends and calyxes from defects on process grading lines is a challenging task due to the complexity of the process. An in-line detection of the apple defect is developed in this article. Firstly, a computer controlled system using three color cameras is placed on the line. In this system, the apples placed on rollers are rotating while moving, and each camera is capturing three images from each apple. In total nine images are obtained for each apple allowing the total surface to be scanned. Secondly, the apple image is segmented from the black background by multi-threshold methods. The defects, including the stem-ends and calyxes, called regions of interest (ROIs), are segmented and counted in each of the nine images. Thirdly, since a calyx and stem-end cannot appear at the same image, an apple is defective if any one of the nine images has two or more ROIs. There are no complex imaging processes or pattern recognition algorithms in this method, because it is only necessary to know how many ROIs are there in a given apple's image. Good separation between normal and defective apples was obtained. The classification error of unjustified acceptance of blemished apples reduced from 21.8% for a single camera to 4.2% for the three camera system, at the expense of rejecting a higher proportion of good apples. Averaged over false positive and false negative, the classification error reduced from 15 to 11%. The disadvantage of this method is that it could not distinguish different defect types. Defects such as bruising, scab, fungal growth, and disease, are treated as the same.
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paper_title: IMAGE ANALYSIS FOR APPLE DEFECT DETECTION

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*** Summary. The objective of this research was to develop and test an image processing system which could identify defects on apple surfaces. A system for identifying surface defects on apples was designed, based on analyzing images acquired while apples were rotating in front of the camera. When multiple images were combined and adjustments made for rotation, dark areas caused by defects would appear with almost the same shape and at the same place in three or more frames. While minimizing false positives, the classifi cation accuracy was very high. The proposed algorithm was effective in detecting various defects such as bruises, frost damage, and scab. The average classifi cation accuracy was 96% for the samples in the experiments.
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paper_title: Classification of vitreousness in durum wheat using soft X-rays and transmitted light images

paper_content:

Abstract Hardness is a kernel characteristic that influences both milling and processing characteristics of wheat. It is one characteristic that is used for segregating wheat to meet the needs for various products. Kernel virteousness is a visual marker for hardness and is the characteristic assessed during the grading process. The potential of classifying vitreous and non-vitreous durum wheat kernels, in crease-down position, using imaging systems based on real time soft X-rays or transmitted light was assessed in this study. Durum wheat kernels at 14, 15 and 16% moisture contents were used as samples in this study. Image features modeling gray level distribution, textural and shape moments were measured and used to develop a classification system for vitreous and non-vitreous durum wheat kernels. The classification accuracies were 76% for vitreous kernels and 82% for non-vitreous kernels at 16% moisture content using the soft X-ray system but for the transmitted light system, the classification accuracies were 86% for vitreous and 93% for non-vitreous kernels. Moisture content had no effect on classifying vitreous or non-vitreous kernels by the transmitted light system but the classification accuracies increased with moisture content for non-vitreous kernels by the soft X-ray system.
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paper_title: Automated strawberry grading system based on image processing

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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: Automatic fruit and vegetable classification from images

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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: Implementation of an Efficient Image Processing Algorithm for Grading Raisins

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Abstract — In order to extract useful features of captured images of bulk raisins by image processing technique, an efficient algorithm was developed and implemented in visual basic language. The proposedmachine vision system consists of a video camera, a capture card, a lighting adjustable tunnel, a personal computer, and mechanical part. The developed algorithm initially extracts the raisins by removing the background from the taken images. It then sorts the raisins according to their RGB color and size features. By a suitable combination of length and RGB color values raisins are graded it two classes.Final step in the algorithm is the calculation of the center of gravity of each raisin to be later used for automatic sorting and rejection of bad raisins. The proposed system was developed and tested and results show that its accuracy is about 96%. Keywords: Grading, Bulk raisin, Image processing, Machine vision, RGB, Color features, Software. * The financial support provided by the University of Tehran, Iran.
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paper_title: Comparison of illuminations to identify wheat classes using monochrome images

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Wheat class identification using machine vision is an objective method which can be used for online testing to automate handling, binning and shipping operations in grain industry. The efficiencies of a monochrome camera-based vision system with three different illuminations (incandescent light (IL), fluorescent ring light (FRL), fluorescent tube light (FTL)) were determined to identify eight western Canadian wheat classes at four moisture levels (11%, 14%, 17% and 20%). The monochrome images of the bulk wheat samples were acquired at each moisture level (3 illuminationsx8 classesx4 moisture contentsx100 replications=9600 images). A linear discriminant function was used for the classification of wheat samples using 32 gray level textural features extracted from the monochrome images. The mean gray values of the wheat classes were in the ranges of 75-103, 73-115, and 107-143 for IL, FRL and FTL, respectively. The mean gray values of wheat classes were significantly different within each illumination and between different illuminations (@a=0.05). Mean gray value was the highest for FTL and the lowest for IL illumination. The moisture content of the wheat samples had significant effect on the mean gray values. The overall classification accuracies were 90%, 81% and 96% for IL, FRL and FTL, respectively, when all the wheat classes were at the same moisture levels. It was 66%, 53% and 85% for IL, FRL and FTL, respectively, when the wheat classes were at different moisture levels. The classification accuracies of a 2-stage classification system for the classes with different moisture levels were 68%, 56% and 90% for IL, FRL and FTL, respectively.
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Title: Applications of Image Processing in Agriculture: A Survey

Section 1: INTRODUCTION
Description 1: Provide an overview of the role of image processing in agriculture, emphasizing the importance and benefits such as increased precision and efficiency in managing farm inputs.

Section 2: APPLICATIONS BASED ON IMAGING TECHNIQUES
Description 2: Discuss various imaging techniques used in agriculture, including Gamma ray imaging, X-ray imaging, UV band imaging, visible and IR band imaging, microwave band imaging, and radio band imaging, along with their specific applications and benefits.

Section 3: Remote Sensing (RS)
Description 3: Explore the use of Remote Sensing in agriculture, detailing the science behind RS, analytical techniques, and various applications including crop inventory, irrigation monitoring, and vegetation mapping.

Section 4: Applications in Weed Detection
Description 4: Discuss the role of image processing in detecting and classifying weeds in agricultural fields, including different methods such as edge detection, color detection, wavelet-based classification, and the use of classifiers like ANN and fuzzy logic.

Section 5: Applications in Fruit/Food Grading
Description 5: Outline the use of image processing for grading and sorting fruits and foods, including techniques for detecting defects, color and size analysis, and the use of computer vision for quality control in food production.

Section 6: CONCLUSION
Description 6: Summarize the effectiveness of image processing techniques in agriculture, highlighting the accuracy of vegetation indices, weed classification, and fruit grading, and conclude with the environmental and economic benefits.