Detection and classification of rice plant diseases

Prajapati, Harshadkumar B.; Shah, Jitesh P.; Dabhi, Vipul K.

doi:10.3233/idt-170301

Cited by 264 publications

(76 citation statements)

References 15 publications

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“…The authors [13] to ensure the extraction of features used the K-means clustering method, preliminarily removing green pixels from the affected parts of the leaf. Traits were extracted into three categories: color, shape and texture, and then SVM were applied to identify rice diseases in a multiclass manner.…”

Section: Introductionmentioning

confidence: 99%

Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

Arinichev¹,

Polyanskikh²,

Волкова³

et al. 2021

IJFIS

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In the article, the authors study the possibility of detecting some fungal diseases of rice using visual computing and machine learning techniques. Leaf blast and brown spot diseases are considered. Modern computer vision methods based on convolutional neural networks are used to identify a particular disease on an image. The authors compare the four most successful and compact convolutional neural network architectures: GoogleNet, ResNet-18, SqueezeNet-1.0, and DenseNet-121. The authors show that in the dataset used for the analysis, the disease can be detected with an accuracy of at least 95%. Testing the algorithm on real data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution. Data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution.

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Section: Introductionmentioning

confidence: 99%

Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

Arinichev¹,

Polyanskikh²,

Волкова³

et al. 2021

IJFIS

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“…For several years, great efforts have been devoted to the study of plant disease detection. Indeed, feature engineering models [3][4][5][6] on one side with Convolutional Neural Networks (CNN) [7][8][9][10] on the other side; are carried out to solve this task.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the study is based on a database of 120 images of infected rice leaves divided into three classes bacterial leaf blight, brown spot, and leaf smut (40 images for each class), Authors have converted the RGB images to an HSV color space to identify lesions, with a segmentation accuracy up to 96.71% using k-means. The experiments were carried out to classify the images based on multiple combinations of the extracted characteristics (texture, color and shape) using Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Models for Tomato Disease Detection

Ouhami

Es-Saady

Hajji

et al. 2020

Lecture Notes in Computer Science

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Vegetable crops in Morocco and especially in the Sous-Massa region are exposed to parasitic diseases and pest attacks which affect the quantity and the quality of agricultural production. Precision farming is introduced as one of the biggest revolutions in agriculture, which is committed to improving crop protection by identifying, analyzing and managing variability delivering effective treatment in the right place, at the right time, and with the right rate.The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. To deal with this problem we consider the deep learning models DensNet, 161 and 121 layers and VGG16 with transfer learning. Our study is based on images of infected plant leaves divided into 6 types of infections pest attacks and plant diseases. The results were promising with an accuracy up to 95.65% for DensNet161, 94.93% for DensNet121 and 90.58% for VGG16.

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“…Further, the authors used knearest neighbour (k-NN) classifier to recognize rice blast and brown spot. Prajapati et al, 2017, proposed centroid feeding based K-means clustering for segmentation and further removed the ineffective spots and used SVM to classify the rice disease. Sanyal et al, 2008, proposed the method of extracting features of the rice diseases i.e.…”

Section: Introductionmentioning

confidence: 99%

Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network

Shrivastava

Pradhan

Minz

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

177

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Early and accurate diagnosis of plant diseases is a vital step in the crop protection system. In traditional practices, identification is performed either by visual observation or by testing in laboratory. The visual observation requires expertise and it may vary subject to an individual which may lead to an error while the laboratory test is time consuming and may not be able to provide the results in time. To overcome these issues, image based machine learning approach to detect and classify plant diseases has been presented in literature. We have focused specifically on rice plant (Oryza sativa) disease in this paper. The images of the diseased symptoms in leaves and stems have been captured from the rice field. We have collected a total of 619 rice plant diseased images from the real field condition belong to four classes: (a) Rice Blast (RB), (b) Bacterial Leaf Blight (BLB), (c) Sheat Blight (SB) and (d) Healthy Leave (HL). We have used a pre-trained deep convolutional neural network (CNN) as a feature extractor and Support Vector Machine (SVM) as a classifier. We have obtained encouraging results. The early identification of rice diseases by this approach could be used as a preventive measure well as an early warning system. Further, it could be extended to develop a rice plant disease identification system on real agriculture field. 1.

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Detection and classification of rice plant diseases

Cited by 264 publications

References 15 publications

Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

Deep Transfer Learning Models for Tomato Disease Detection

Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network

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