Title: Disease detection in different plants to save the environment using IOT, image processing and machine learning: a review

Authors: Prince Rajpoot; Vishal Singh Chandel; Amit Kumar Pandey; Shivendu Mishra; Vikas Patel; Shrish Bajpai; Digvijay Pandey

Addresses: Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Applied Science and Humanities, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Applied Science and Humanities, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Electrical Engineering, Rajkiya Engineering College, Ambedkar Nagar, India ' Department of Electronics and Communication Engineering, Integral University, India ' Department of Technical Education, IET, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India

Abstract: Diseases, insects and weeds together hinders the crop productivity and destroy crops. A vital point that needs to be tackled for economic growth and product development is detecting various plant diseases at an early stage. Classifying and identifying diseases using traditional methods is a difficult task that takes a lot of time, excessive work, extensive knowledge about plant diseases, and continuous farm monitoring. Therefore, plant diseases must be automatically identified and classified, which can be accomplished by utilising the internet of things (IoT) and some machine learning algorithms. Through automation, IoT overcomes human involvement in a particular task. Various IoT sensing devices such as thermal sensors and soil moisture sensors including colour sensors and humidity sensors can be used to detect the presence of plant disease. It helps in selective spraying and prevents to use the automatic medicine spraying process which affects the surrounding environment.

Keywords: image processing; internet of things; IoT; machine learning; plant disease detection.

DOI: 10.1504/IJGW.2023.134911

International Journal of Global Warming, 2023 Vol.31 No.4, pp.371 - 412

Received: 18 Feb 2023
Accepted: 07 Apr 2023
Published online: 17 Nov 2023 *

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