Rice Blast Disease Detection and Classification Using Machine Learning Algorithm

Ramesh, S.; Vydeki, D.

doi:10.1109/icmete.2018.00063

Cited by 72 publications

(23 citation statements)

References 6 publications

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“…VGG-19 is a 19-layer deep CNN. You may use the ImageNet database [17], that has been trained on over a million photographs, to import a pre-trained version of the network. The network can sort photographs into 1000 distinct object categories.…”

Section: Vgg-19mentioning

confidence: 99%

“…(Azathet al,2021)[10], this research paper presents the work on the cotton leaf for the detection of diseases and pest diagnosis using image processing and Deep Learning. The researchers used CNN to detect diseases in cotton leaves.In terms of recognizing specific diseases, the model has a 96.4 percent accuracy rate.Recently, different deep learning methods[11][12][13][14][15][16][17] have been applied with Convolutional neural networks and computer vision to detect plant disease, recognition of plant leaves for medicine purposes, pest detection, counting of the wheat head,etc. In real-time robots need to detect the plant leaf disease and provide proper pesticide over it in large farms and weed detection with a bounding box helps to remove the weed with herbicides.…”

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confidence: 99%

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Rice Leaf Disease Classification Using Cnn

Tejaswini

Singh

Ramchandani

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Rice is amongst the majorly cultivated crops in India and its leaf diseases can have a substantial impact on output and quality. The most important component is identifying rice leaf diseases, which have a direct impact on the economy and food security. Brown spot, Leaf Blast, Hispa are the most frequently occurring rice leaf diseases. To resolve this issue, we have studied various machine learning and deep learning approaches for detecting the diseases on their leaves by calculating their accuracy, recall, and precision to measure the performance. This study helps the farmers by detecting the diseases in rice leaves in order to get a healthy crop yield. The deep learning models perform well when compared with the machine learning methods. After analyzing all of the deep learning models, we found that the 5-layer convolution model had the best accuracy of 78.2 %, while others, such as VGG16, had a lower accuracy of 58.4%.

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Section: Vgg-19mentioning

confidence: 99%

mentioning

confidence: 99%

Rice Leaf Disease Classification Using Cnn

Tejaswini

Singh

Ramchandani

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…We used a total of 21 research papers of the last eight years on rice leaf and seedling disease by considering parameters such as segmentation type, segmentation techniques, features extracted, dataset size, and image background. [7] Not Specified Not Specified RGB values 60 images [19] Clustering K-means Color, shape, and texture Not specified. [20] Clustering K-means Color, texture, and shape Not specified [15] Clustering K-means Shape and color Not specified [5] Clustering K-means Area , texture descriptors using GLCM , and color moments…”

Section: Summary Of Image Processing Techniques Used In Rice Disease Detectionmentioning

confidence: 99%

A Survey on Rice Leaf and Seedlings Disease Detection System

Chaudhari¹,

Kanagasabai²

2021

INDJCSE

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Rice is a major crop in India. Near about 70%, the economy depends on agriculture products. However, Agriculture production is uncertain due to natural calamities, environmental conditions, and unpredictable plant diseases. Plant diseases are nearly impossible to recognize by the open eye by the farmers and crop producers. Therefore, an automatic detection system is a modern approach. Many automatic plant disease detection systems are developing by the researchers. In this paper, we present a summary of different image processing and machine learning techniques applied to the recognition of rice leaf and seedling diseases. Various attributes considered for doing a survey include segmentation type, segmentation techniques, features extracted, dataset size, author's name, and publication year, category of disease, techniques used, detection/classification accuracy, and future scope/limitations. We have gone through several research papers and a brief review of recent image processing and machine-learning techniques used by the researchers has been provided for the rice leaf and seedlings disease detection and classification.

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“…Plant diseases can be detected using a machine learning system. For the new method being established, photographs of vigorous and blight-infected leaves are taken recommended by (Ramesh & Vydeki, 2018). The characteristics of the rice leaf's healthy and sick portions are considered.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Pests and diseases are currently causing cotton yields to drop from 5% to 15%. Losses of up to 50% may result from a lack of safety and control procedures (Ramesh & Vydeki, 2018;Virnodkar, Pachghare, Patil, & Jha, 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based Prediction of Cotton farming using ARIMA Model

Malik

Umar

2021

iRASD J. Comp. & Info Tech.

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Disease detection in agriculture is critical in severe weather conditions. Keeping an eye on many cotton fields is challenging for farmers. Deep learning and image processing identify agricultural ailments before they spread and further damage. Cotton crop with the most important correlation quantities among pest disease existence are inspected by predictors. In this study, transfer learning methods for analysis are checked. The machine learning approach developed accurately distinguishes healthy and unhealthy plants. As a result, the proposed model may be used to monitor a wide range of areas for more rapid analysis and action, leading to increased productivity. It is possible to anticipate a timeline using ARIMA. A long-term study reveals that the value swipes widely during short and long periods due to random influences. It is also inspected how the recommended approach is affected by using several cotton crop characteristics. In addition to separating green crops from other crops in the field, this procedure can also separate harvests such as Cotton from other crops.

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Rice Blast Disease Detection and Classification Using Machine Learning Algorithm

Cited by 72 publications

References 6 publications

Rice Leaf Disease Classification Using Cnn

Rice Leaf Disease Classification Using Cnn

A Survey on Rice Leaf and Seedlings Disease Detection System

Machine learning-based Prediction of Cotton farming using ARIMA Model

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