Plant Diseases Detection and Classification using Machine Learning Models
01 Dec 2019-
TL;DR: An overview of image segmentation using K-means clustering and HSV dependent classification for recognizing infected part of the leaf and feature extraction using GLCM is presented.
Abstract: In the agricultural sector, identification of plant diseases is extremely crucial as they hamper robustness and health of the plant which play a vital role in agricultural productivity. These problems are common in plants, if proper prevention methods are not taken it might seriously affect the cultivation. The current method of detecting disease is done by an expert's opinion and physical analysis, which is time-consuming and costly in the real world. Hence, computer-based detection has become a necessity. This paper comprises of an overview of image segmentation using K-means clustering and HSV dependent classification for recognizing infected part of the leaf and feature extraction using GLCM. The efficiency of the proposed methodology is able to detect and classify the plant diseases successfully with an accuracy of 98% when processed by Random Forest classifier.
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TL;DR: According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping.
Abstract: With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.
54 citations
TL;DR: In this paper, a type of convolutional neural network (CNN) was used with transfer learning approach for recognizing diseases in rice leaf images and obtained a good accuracy of 95.67%.
47 citations
01 Jul 2021
27 citations
TL;DR: The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing that outperforms the existing methods.
Abstract: Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system's total performance reached 94% at its best. The proposed approach outperforms the existing methods.
23 citations
TL;DR: In this article, a new model using mobile video image processing and Long Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability.
Abstract: The disease infliction of the plants severely influences the yield. It alters the essence and extent of crop production cause fiscal distress. Consequently, the diagnosis of numerous plant diseases is significant to decrease the yield perdition by discovering crop infections at their earlier stages. This paper introduces a new model using mobile video image processing and Long-Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability. The rice plant videos captured under uncontrolled conditions in day-lighting using a mobile handset and divided into two sections for the designing and testing of LSTM-SRNN models. After shooting, the video images of the rice plant segmented using colour indexing and linear color space transformation with minimal daylight impact. Low-level spatial features; entropy, standard deviation, and fuzzy features extracted after video image segmentation. The excerpted characteristics with the composite combinations transformed in time-series datasets with the desired response. The datasets employed in the LSTM-SRNN model for progressive learning. The distinct test video features applied in LSTM-SRNN to appraise the generalization capability of the proposed system with performance analysis. The experimental outcomes of the proposed LSTM-SRNN model exhibit 99.99% prediction ability with fuzzy features. The model also presents possibilities for dynamic learning adaptability and temporal information processing to overcome the limitations of many well-known rule-based and machine learning approaches.
16 citations
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37,017 citations
"Plant Diseases Detection and Classi..." refers methods in this paper
...Otsu method was used for segmentation of leaf image [4-6]....
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01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442 citations
"Plant Diseases Detection and Classi..." refers methods in this paper
...GLCM is the statistical method of exploring texture, which considers the spatial bonding of pixels [20]....
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TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
4,543 citations
"Plant Diseases Detection and Classi..." refers methods in this paper
...Otsu method was used for segmentation of leaf image [4-6]....
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TL;DR: In this article, the authors present a review of the currently used technologies that can be used for developing a ground-based sensor system to assist in monitoring health and diseases in plants under field conditions.
965 citations
"Plant Diseases Detection and Classi..." refers background in this paper
...In addition, studies involving traditional machine learning methods are generally complicated or based on outdated feature extraction [13-16], and costly camera photography is required[17]....
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TL;DR: An algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases and also covers survey on different diseases classification techniques that can be used for plant leaf disease detection.
699 citations
"Plant Diseases Detection and Classi..." refers result in this paper
...According to the results it is proved that Random Forest classifier has enhanced the plant disease detection process as compared to approaches used in [22]....
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