Plant Disease Detection using Machine Language

DOI

10.2991/978-94-6463-718-2_109How to use a DOI?

Keywords

Plant disease detection; machine learning; deep learning; real-time implementation; multi-disease detection; explainable AI

Abstract

Effective, accurate diagnosis and management methods that are rapid, robust, and scalable are integral to combating the emerging crisis of plant diseases that threaten food security worldwide. This study presents an improvement of deep learning techniques in detecting plant disease, closing the relevant gaps within existing solutions, regarding dataset versatility, real time implementation and multi disease identification. Using a diverse balanced dataset, the model acts consistently at various environmental conditions, making it practical in terms of real-world scenarios. It is affordable to small farms, using lightweight, explainable AI architectures that are designed for mobile and edge compute environments. Integrating with precision agriculture tools (e.g., drones and IoT devices) improves scalability, making it suitable for large-scale farming operations. Furthermore, embedding semi-supervised learning methodologies minimizes the resource cost of labeling the dataset, ensuring long-term sustainability. This study demonstrates a significant improvement in the field of precision agriculture, creating a robust, thoroughly-documented economic system that meets the needs of farming in the 21st century.

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  - S. Ajithkumar
AU  - M. Jayanthi
AU  - P. Priyadharshini
AU  - M. S. Divya
AU  - M. Farhana Parveen
AU  - A. Gayathri
PY  - 2025
DA  - 2025/05/23
TI  - Plant Disease Detection using Machine Language
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1309
EP  - 1319
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_109
DO  - 10.2991/978-94-6463-718-2_109
ID  - Ajithkumar2025
ER  -