A Comprehensive Analysis of Machine Learning and Deep Learning Approaches in Detecting Plant Disease Diagnosis

DOI

10.2991/978-94-6239-697-5_5How to use a DOI?

Keywords

Generative AI; CNN; Deep Learning

Abstract

Plant diseases create major problems for agricultural production which threatens food security throughout the world [1], [11]. Current disease identification methods based on human interference face challenges because they consume time while depending on human decision and lack the ability to handle large number of samples. Detection of plant diseases through analysis of image has become possible because of recent machine learning (ML) & deep learning (DL) advancements which use convolutional neural networks (CNNs) for automated and correct disease identification [1], [4], [6]. The process of effective crop management needs identification as well as assessment of severity of disease and correct treatment protocols. The research provides an extensive evaluation of ML and DL methods which detect plant diseases and measure their severity and suggest appropriate treatments. The paper presents a general description of detection pipelines to establish the current state of detection methods while avoiding technical explanations of their operation. The review conducts a comparative analysis of previous research to determine vital knowledge deficits which stem from insufficient connections between disease identification and severity assessment and treatment guidance. The research demonstrates that agricultural decision-making requires deployable plant disease management systems which should operate as a single unified system.

Copyright

© 2026 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  - Akansha Agrawal
AU  - Ajay Suri
AU  - Kimmi Verma
PY  - 2026
DA  - 2026/06/04
TI  - A Comprehensive Analysis of Machine Learning and Deep Learning Approaches in Detecting Plant Disease Diagnosis
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 40
EP  - 48
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_5
DO  - 10.2991/978-94-6239-697-5_5
ID  - Agrawal2026
ER  -