Identification of Diseases in Paddy Crops Using Cnn

Author:

Parasa Gayatri1,Arulselvi M.1,Razia Shaik2

Affiliation:

1. Annamalai University

2. Koneru Lakshmaiah Education Foundation

Abstract

Abstract

In ancient times, agriculture is one of the most predominant occupations of Indian civilizations and it has a great impact in contributing to our country’s economy. Unfortunately, due to several reasons like pests and unpredictable climatic conditions, there has been poor productivity in certain crops, especially paddy. This has been drawn attention towards enhancing the productivity of the paddy crops. Through lots of research, it has been identified that paddy crops are infected by various diseases, and this is one of the reasons that directly affects the overall productivity of the crop. Hence, there emerged an immediate need to take preventive measures and improve the overall productivity rate of paddy crop. In this regard, an Intelligent deep learning algorithm called Convolution Neural Network (CNN) is proposed with an increased structure of 15 layers which predict various diseases that may affect the rice leaves. The developed model efficiency was evaluated in terms of Accuracy, Precision, F-measure, and Recall.

Publisher

Research Square Platform LLC

Reference24 articles.

1. Das, S. (2021). A model for probabilistic prediction of paddy crop disease using convolutional neural network. Intelligent and Cloud Computing (pp. 125–134). Springer.

2. Hossain, K., Asif (2021). Paddy Disease Prediction Using Convolutional Neural Network. International Conference on Intelligent Computing & Optimization. Springer, Cham.

3. Kodaty, S. C., & BalajiHalavath (2021). A New Approach for Paddy Leaf Blast Disease Prediction Using Logistic Regression. Advances in. Information Communication Technology and Computing (pp. 533–542). Springer.

4. Anandhan, K. (2021). and Ajay Shanker Singh. Detection of paddy crops diseases and early diagnosis using faster regional convolutional neural networks. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE.

5. Leelavathy, B., & Ram Mohan Rao Kovvur. (2021). and. Prediction of biotic stress in paddy crop using deep convolutional neural networks. Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer, Singapore.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3