Monitoring and controlling rice diseases using Image processing techniques

Joshi, Amrita A.; Jadhav, B. D.

doi:10.1109/cast.2016.7915015

Cited by 112 publications

(29 citation statements)

References 8 publications

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“…Remotely Sensed Data and Vegetation Indices: Hyperspectral Images (Band 1 ∼ 4) [80], [81], C-Band Synthetic Aperture Radar (SAR) [40] Drones Based Data: High-resolution Images [37], [39], [87]- [92], [118], [119] Monitoring paddy rice growth Remotely Sensed Data and Vegetation Indices: LSWI [63],EVI [62], [63], NDVI [63], [66], [69], LAI [69], [71]- [73], C-Band Synthetic Aperture Radar (SAR) [83] Assessing quality of paddy rice Based on these spectral bands, several measurements can be derived and computed such as Land Surface Water Index (LSWI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) (see Table 2).…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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“…Amrita A. Joshi et al [10] focused mainly on rice disease like bacterial blight, rice blast, brown spot and sheath Blight. During Feature Extraction, they extract color and shape as feature vector of Diseased Rice Images.…”

Section: Iiiliterature Reviewmentioning

confidence: 99%

Detection of Rice Disease Using Bayes’ Classifier and Minimum Distance Classifier

Sharma¹,

Mir²,

Sarwr³

2020

J Multimed Inf Syst

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Rice (Oryza Sativa) is an important source of food for the people of our country, even though of world also .It is also considered as the staple food of our country and we know agriculture is the main source country's economy, hence the crop of Rice plays a vital role over it. For increasing the growth and production of rice crop, groundbreaking technique for the detection of any type of disease occurring in rice can be detected and categorization of rice crop diseases has been proposed in this paper. In this research paper, we perform comparison between two classifiers namely MDC and Bayes' classifiers Survey over different digital image processing techniques has been done for the detection of disease in rice crops. The proposed technique involves the samples of 200 digital images of diseased rice leaf images of five different types of rice crop diseases. The overall accuracy that we achieved by using Bayes' Classifiers and MDC are 69.358 percent and 81.06 percent respectively.

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“…In addition, it will help the farmers to select the correct pesticides. 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).…”

Section: Introductionmentioning

confidence: 99%

“…color and texture and these features were fed to multilayer perceptron (MLP) to detect different rice plant diseases. Joshi et al, 2016, extracted the color and shape features of the leaf and used these features to minimum distance classifier (MDC) and k-NN Classifier to detect the diseases. In the same line, Yao et al, 2009, proposed the combination of texture and shape features of the rice leaf to detect the different rice diseases using the SVM classifier.…”

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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Monitoring and controlling rice diseases using Image processing techniques

Cited by 112 publications

References 8 publications

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Detection of Rice Disease Using Bayes’ Classifier and Minimum Distance Classifier

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

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