Image recognition of plant diseases based on principal component analysis and neural networks
29 May 2012-pp 246-251
TL;DR: The results showed that neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained.
Abstract: Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.
Citations
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TL;DR: A survey on the different methods relevant to citrus plants leaves diseases detection and the classification reveals that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy and new tools are needed to fully automate the detection and Classification processes.
251 citations
TL;DR: Experimental results on the datasets of six common cucumber leaf diseases demonstrate that the proposed GPDCNN model can effectively recognize cucumber diseases.
182 citations
TL;DR: The need of simple plant leaves disease detection system that would facilitate advancements in agriculture is reviewed and several steps viz. image acquisition, image pre-processing, features extraction and neural network based classification are reviewed.
Abstract: Diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Now a day's plant diseases detection has received increasing attention in monitoring large field of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of plant diseases. In this paper we review the need of simple plant leaves disease detection system that would facilitate advancements in agriculture. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. This technique will improves productivity of crops. This paper also compares the benefits and limitations of these potential methods. It includes several steps viz. image acquisition, image pre-processing, features extraction and neural network based classification.
164 citations
Cites methods from "Image recognition of plant diseases..."
...There are various techniques for image segmentation discuss below....
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TL;DR: A comprehensive discussion on the diseases detection and classification performance is presented based on analysis of previously proposed state of art techniques particularly from 1997 to 2016.
Abstract: In this paper, we address a comprehensive study on disease recognition and classification of plant leafs using image processing methods. The traditional manual visual quality inspection cannot be defined systematically as this method is unpredictable and inconsistent. Moreover, it involves a remarkable amount of expertise in the field of plant disease diagnostics (phytopathology) in addition to the disproportionate processing times. Hence, image processing has been applied for the recognition of plant diseases. The paper has been divided into two main categories viz. detection and classification of leafs. A comprehensive discussion on the diseases detection and classification performance is presented based on analysis of previously proposed state of art techniques particularly from 1997 to 2016. Finally, discussed and classify the challenges and some prospects for future improvements in this space.
139 citations
Cites methods from "Image recognition of plant diseases..."
...[82] described a method for the identification of diseases in grape and wheat leafs....
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TL;DR: The research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.
Abstract: Black rot, Black measles, Leaf blight and Mites of grape are four common grape leaf diseases that seriously affect grape yield. However, the existing research lacks a real-time detecting method for grape leaf diseases, which cannot guarantee the healthy growth of grape plants. In this article, a real-time detector for grape leaf diseases based on improved deep convolutional neural networks is proposed. This article first expands the grape leaf disease images through digital image processing technology, constructing the grape leaf disease dataset (GLDD). Based on GLDD and the Faster R-CNN detection algorithm, a deep-learning-based Faster DR-IACNN model with higher feature extraction capability is presented for detecting grape leaf diseases by introducing the Inception-v1 module, Inception-ResNet-v2 module and SE-blocks. The experimental results show that the detection model Faster DR-IACNN achieves a precision of 81.1% mAP on GLDD, and the detection speed reaches 15.01 FPS. This research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.
123 citations
References
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TL;DR: This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes the current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted.
Abstract: Reliable, precise and accurate estimates of disease severity are important for predicting yield loss, monitoring and forecasting epidemics, for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including co-evolution. Disease assessments that are inaccurate and/or imprecise might lead to faulty conclusions being drawn from the data, which in turn can lead to incorrect actions being taken in disease management decisions. Plant disease can be quantified in several different ways. This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes our current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted. The review also considers how these can be identified using various statistical tools. Indeed, great strides have been made in the last thirty years in identifying the sources of assessment error inherent to visual rating, and this review highlights ways that assessment errors can be reduced—particularly by training raters or using assessment aids. Lesion number in relation to area infected is known to influence accuracy and precision of visual estimates—the greater the number of lesions for a given area infected results in more overestimation. Furthermore, there is a widespread tendency to overestimate disease severity at low severities (<10%). Both interrater and intrarater reliability can be variable, particularly if training or rating aids are not used. During the last eighty years acceptable accuracy and precision of visual disease assessments have often been achieved using disease scales, particularly because of the time they allegedly save, and the ease with which they can be learned, but recent work suggests there can be some disadvantages to their use. This review considers new technologies that offer opportunity to assess disease with greater objectivity (reliability, precision, and accuracy). One of these, visible light photography and digital image analysis has been increasingly used over the last thirty years, as software has become more sophisticated and user-friendly. Indeed, some studies have produced very accurate estimates of disease using image analysis. In contrast, hyperspectral imagery is relatively recent and has not been widely applied in plant pathology. Nonetheless, it offers interesting and potentially discerning opportunities to assess disease. As plant disease assessment becomes better understood, it is against the backdrop of concepts of reliability, precision and accuracy (and agreement) in plant pathology and measurement science. This review briefly describes these concepts in relation to plant disease assessment. Various advantages and disadvantages of the different approaches to disease assessment are described. For each assessment method some future research priorities are identified that would be of value in better understanding the theory of disease assessment, as it applies to improving and fully realizing the potential of image analysis and hyperspectral imagery.
636 citations
"Image recognition of plant diseases..." refers methods in this paper
...The studies on the recognition and the automatic assessment of plant diseases based on image processing have been reported [2], [3], [4], [5], [6], [7], [8], [9], [10]....
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TL;DR: The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases and can achieve 20% speedup over the approach proposed in [1].
Abstract: We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].
471 citations
TL;DR: In this article, the color co-occurrence method (CCM) was used to determine whether texture based hue, saturation, and intensity (HSI) color features in conjunction with statistical classification algorithms could be used to identify diseased and normal citrus leaves under laboratory conditions.
310 citations
"Image recognition of plant diseases..." refers background in this paper
...Generally, the extracted features from the images of plant diseases include color features [12], [19], [22], [23], shape features [24], texture features [25], and so on....
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TL;DR: In this paper, a machine vision system for the identification of the visual symptoms of plant diseases, from coloured images, was reported, where Diseased regions shown in digital pictures of cotton crops were enhanced, segmented, and a set of features were extracted from each of them.
268 citations
"Image recognition of plant diseases..." refers methods in this paper
...Image recognition of plant diseases is to extract the characteristic feature information from the diseased regions in the obtained images by using image processing techniques, and then to achieve disease recognition by using pattern recognition methods such as discriminant analysis [11], [12], neural networks [9], [13], [14], [15], [16] and support vector machine [17], [18], [19], [20], [21]....
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TL;DR: The study was successful in developing a color classifier and a knowledge domain based on color for future development of intelligent automated grain grading systems.
Abstract: Symptoms associated with fungal damage, viral diseases, and immature soybean (Glycine max) seeds were characterized using image processing techniques. A Red, Green, Blue (RGB) color feature-based multivariate decision model discriminated between asymptomatic and symptomatic seeds for inspection and grading. The color analysis showed distinct color differences between the asymptomatic and symptomatic seeds. A model comprising six color features including averages, minimums, and variances for RGB pixel values was developed for describing the seed symptoms. The color analysis showed that color alone did not adequately describe some of the differences among symptoms. Overall classification accuracy of 88% was achieved using a linear discriminant function with unequal priors for asymptomatic and symptomatic seeds with highest probability of occurrence. Individual classification accuracies were asymptomatic 97%, Alternaria spp. 30%, Cercospora spp. 83%, Fusarium spp. 62%, green immature seeds 91%, Phomopsis spp. 45%, soybean mosaic potyvirus (black) 81%, and soybean mosaic potyvirus (brown) 87%. The classifier performance was independent of the year the seed was sampled. The study was successful in developing a color classifier and a knowledge domain based on color for future development of intelligent automated grain grading systems.
76 citations
"Image recognition of plant diseases..." refers methods in this paper
...The studies on the recognition and the automatic assessment of plant diseases based on image processing have been reported [2], [3], [4], [5], [6], [7], [8], [9], [10]....
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