2021
DOI: 10.4018/ijismd.2021010101
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Plant Leaf Disease Detection Using CNN Algorithm

Abstract: Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detect… Show more

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Cited by 61 publications
(30 citation statements)
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“…The DenseNet121 model achieved an accuracy of 97.11% on tomato disease classification. In [ 20 ], the authors proposed a custom convolutional neural network for plant disease classification. The custom network achieved a classification accuracy of 94.5% on the test dataset.…”
Section: Literature Surveymentioning
confidence: 99%
“…The DenseNet121 model achieved an accuracy of 97.11% on tomato disease classification. In [ 20 ], the authors proposed a custom convolutional neural network for plant disease classification. The custom network achieved a classification accuracy of 94.5% on the test dataset.…”
Section: Literature Surveymentioning
confidence: 99%
“…The proposed ABNR model works are mechanized to identify the features based on the significance using the self-attention layer through the weighted approach, and aggregation is performed in the neural ranking process. The statements are then ranked based on the severity of the vulnerability using the softmax layer of the neural ranking model [32]. The block diagram of the proposed model is presented in Figure 2 [33].…”
Section: Proposed Modelmentioning
confidence: 99%
“…The epidermis, dermis, and subcutaneous tissues comprise it. The skin senses the external environment and protects our inside organs and tissues from harmful microorganisms, pollution, and sun exposure [ 1 ]. Numerous environmental and internal variables may affect the skin.…”
Section: Introductionmentioning
confidence: 99%
“…Researchers [ 1 ] have incorporated numerous methods for recognizing and classifying skin disorders that have been automated. Most diagnostic techniques depend on machine vision, although epidermis identification of these skin disorders does not need radiological imaging.…”
Section: Introductionmentioning
confidence: 99%