Comprehensive Studies for Sustainable Agriculture Using Plant Disease Detection

DOI

10.2991/978-94-6239-685-2_23How to use a DOI?

Keywords

Sustainable Agriculture; IoT; Image Processing; Data Analysis; ICT Plant Disease Detection; AI

Abstract

Agriculture is not just a business sector; it is the backbone of a country’s economy and GDP [Gross Domestic Product], both in developing and developed nations. Precision agriculture makes the sector more sustainable and aims towards the achievement of the Sustainable Development Goals [SDGs]. The integration of cutting-edge technology with agriculture is transforming traditional farming into digital agriculture. This revolution enhances agricultural productivity; however, various types of diseases continue to pose challenges due to multiple factors. Several techniques have been developed and proposed for detecting plant disease, including IoT-enabled systems, image processing, machine learning, and AI-based approaches, many of which are non-destructive. This article presents a comparative study of disease detection methods using image processing approaches. Imaging techniques include hyperspectral imaging, thermal imaging, multispectral imaging, and fluorescence imaging. The spectral signatures information related to healthy and ill plants is used in processing imaging techniques. The methodologies of image processing for disease detection are discussed in this article, along with a comprehensive review and discussion of different approaches. Efficient agricultural outcomes largely depend on effective plant health monitoring. Implementing disease detection systems can help to minimize grain loss caused by poor quality and support the growing demand for food. The comparative analysis gives an idea about the strengths and limitations of each technique, thereby guiding future research and enabling practical applications in precision agriculture for sustainable crop production.

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  - Kirti Prakash Bhure
AU  - Ajay Kumar Vyas
PY  - 2026
DA  - 2026/05/26
TI  - Comprehensive Studies for Sustainable Agriculture Using Plant Disease Detection
BT  - Proceedings of the International Conference on Infrastructure Development and Sustainability (ICIDS 2025)
PB  - Atlantis Press
SP  - 415
EP  - 430
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6239-685-2_23
DO  - 10.2991/978-94-6239-685-2_23
ID  - Bhure2026
ER  -