An Enhancement of Plant Disease Detection using Decision Support System (DSS) with Machine Learning

DOI

10.2991/978-94-6463-787-8_6How to use a DOI?

Keywords

Decision Support System (DSS); Plant Disease; Random Forest (RF); K-Nearest Neighbour (KNN); Decision Tree (DT); Convolutional Neural Network (CNN); Support Vector Machine (SVM)

Abstract

Numerous plant diseases have a significant effect on the ecosystem through both qualitative and quantitative losses in crop fields. In today’s technologically advanced world, automation of plant disease detection becomes increasingly crucial as food demand steadily rises. Time limits, expensive expenses, and the unavailability of experts in remote places are among the difficulties associated with depending exclusively on human expertise for disease diagnosis. Nonetheless, it is more affordable and takes much less time to correctly identify and distinguish between plant diseases when leaf images are used. Various techniques for identifying plant diseases using machine learning have been developed by different researchers. Nonetheless, there hasn’t been enough discussion on how to pick the finest technique for recognizing a particular leaf disease. This article addresses the detection of diseases using support vector machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), and Convolutional Neural Network (CNN) among other machine learning approaches. The analysis provides a thorough comparison evaluation by concentrating on important characteristics like accuracy, recall, F score and precision. The ultimate choice of the best machine learning algorithm for identifying plant leaf diseases can be made by utilizing a Decision Support System (DSS), guaranteeing the most favorable decision-making in this field.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sourav Chatterjee
AU  - Moumita Chatterjee
AU  - Sudakshina Dasgupta
AU  - Indrajit Bhattacharya
PY  - 2025
DA  - 2025/07/17
TI  - An Enhancement of Plant Disease Detection using Decision Support System (DSS) with Machine Learning
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 50
EP  - 63
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_6
DO  - 10.2991/978-94-6463-787-8_6
ID  - Chatterjee2025
ER  -