A survey on detection and classification of rice plant diseases

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

doi:10.1109/icctac.2016.7567333

Cited by 130 publications

(44 citation statements)

References 14 publications

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“…Among the applications analyzed, this review analyses aspects like identification of foreign matter, insect infestation, microbial infection, and discolored grains. Shah et al (2016) presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of infected rice plants. The survey is organized in two main parts.…”

Section: Introductionmentioning

confidence: 99%

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Patrício

Rieder

2018

Computers and Electronics in Agriculture

702

257

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Grain production plays an important role in the global economy. In this sense, the demand for efficient and safe methods of food production is increasing. Information Technology is one of the tools to that end. Among the available tools, we highlight computer vision solutions combined with artificial intelligence algorithms that achieved important results in the detection of patterns in images. In this context, this work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley. In this sense, we present 25 papers selected in the last five years with different approaches to treat aspects related to disease detection, grain quality, and phenotyping. From the results of the systematic review, it is possible to identify great opportunities, such as the exploitation of GPU (Graphics Processing Unit) and advanced artificial intelligence techniques, such as DBN (Deep Belief Networks) in the construction of robust methods of computer vision applied to precision agriculture.

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Section: Introductionmentioning

confidence: 99%

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Patrício

Rieder

2018

Computers and Electronics in Agriculture

702

257

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“…Along this line, image based plant disease identification and classification models have been developed in literature for various plants such as (Liu et al, 2009;Phadikar et al,2013;Joshi et al, 2016;Jia et al, 2013;Hu et al, 2016;Cao et al, 2012). Among different crops, rice is the major crop in Asian country (Liu et al, 2010) including India (Shah et al, 2016). In this paper, we have focused specifically on rice plant disease identification and classification.…”

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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“…Agriculture is a very important supply of the survival in India [1]. It is complete a major role within the improvement of human civilization.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Disease Detection in Plant using Image Processing Techniques

Gupta¹

2019

IJRASET

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We are familiar with this fact that there are major production and economic losses due to the disease in plants; a disease also degrades the quality of Agricultural Products as well as the quantity of production. Now a day's farmers are facing many problems, they are always confused to use the different type of disease detection techniques. The paper shows various steps of image processing which covers Image acquisition followed by pre-processing then segmentation along with feature extraction and then classification for identification and discovering various plant diseases in the leaf's of the plants.

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A survey on detection and classification of rice plant diseases

Cited by 130 publications

References 14 publications

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

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

A Survey on Disease Detection in Plant using Image Processing Techniques

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