FLORA AND FAUNA ISSN 2456-9364(Online) 0971-6920(Print)
         
2022 Vol.28 No.1 PP 77­-85
https://doi.org/10.33451/florafauna.v28i1pp77-85
Plants Disease Detection Using Deep Learning Plant Disease CNN Models for Image Segmentation
*Abhinav Agarwal, D.K.Agarwal1, T.K. Sharma2, Vijay Kumar Yadav3 and Mukesh Srivastava1
*Department of Computer Science,
Singhania University,
JHUNJHUNU (RAJASTHAN), INDIA
1Department of Chemistry,
2Department of Botany,
3Department of Zoology,
Bipin Bihari College,
JHANSI (U.P.) INDIA
*Corresponding Author : Email : abhinavkiot2410@gmail.com
ABSTRACT
For increased agricultural productivity, early diagnosis and management of plants illness are critical. The texture, color, and spots of a diseased plant’s leaves may be used to distinguish it from a healthy one. Observing leaves a traditional way requires a certain level of experience. Farmers that lack experience and resources might benefit from the creation of plant illness detection utilizing Deep Learning methods. Deep Learning methods are used in this study to categorise the various plant illnesses. Due to its great success in image-based classifications, the plant illness convolutional neural network (CNN) construction is deployed. When compared to manual observation of leaves, the Deep Learning models are quicker and more precise. The Plant Pathology 2020 FGVC7 data set is used to train the PD CNN framework in this study. The model’s 95 percent accuracy is the best among them.

Key words : Convolutional Neural Networks, Data Augmentation, Deep Learning, Plant disease.
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