Enhancing Crop Disease Identification Using Machine Learning

DOI

10.2991/978-94-6463-948-3_73How to use a DOI?

Keywords

Machine Learning; Deep Learning; Crop Disease Detection; Ginger Crop; Image Processing; IoT; Hybrid Models

Abstract

The swift development of machine learning (ML) and deep learning (DL) methods has played an important role in changing the practice of agriculture especially in the context of early detection and identification of crop diseases. Timely and correct diagnosis of diseases is essential in the minimization of crop yield losses, pesticide applications and ensuring food security. The proposed paper includes a full literature review of recent papers applying the ML and DL methodologies to crop disease detection, with a particular focus on ginger disease detection. The systematic evaluation identifies several main research problems, such as the scarcity of data sets and standard benchmarks, the inability to generalize models to new environments, the inability to deploy models in real time and the inability to integrate them with IoT systems. The review summarizes the available results, compares various methodological strategies and also suggests a conceptual framework to combine the hybrid ML-DL models with IoT-driven monitoring to generate scalable, precise, and real-time agricultural applications. The presented insights will help guide researchers and practitioners to design the next generation of intelligent farming solutions.

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  - Trupti Shinde
AU  - Sanjesh Pawale
PY  - 2026
DA  - 2026/01/06
TI  - Enhancing Crop Disease Identification Using Machine Learning
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 1071
EP  - 1079
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_73
DO  - 10.2991/978-94-6463-948-3_73
ID  - Shinde2026
ER  -