Deep Learning-Based Crop Disease Detection: A Comprehensive Review of ResNet Architectures

DOI

10.2991/978-94-6239-713-2_27How to use a DOI?

Keywords

ResNet; Crop Disease Detection; PlantVillage Dataset; Deep Learning; Convolutional Neural Network (CNN); Smart Agriculture; Explainable AI (XAI); Attention Mechanism

Abstract

Agriculture is regarded as one of the largest pillars in the global economy, as it has a direct impact on food security, livelihoods in rural areas, and the general development of the nation. But the issue of crop diseases has become a constant bane, and the loss of yield and economic losses to farmers globally have been experienced. The Food and Agriculture Organisation (FAO) estimates that a huge proportion of the annual yield reduction in the world is caused by plant diseases, which lead to billions of dollars in losses. Early and accurate detection of these diseases is important for reducing their effects and encouraging sustainable agriculture. With the fast development of artificial intelligence (AI) and computer vision technologies, the agricultural landscape has been altered, as it allows automating the most important farming procedures, especially plant disease identification. Convolutional Neural Networks (CNNs) are considered the most popular method of image-based disease classification among other deep learning methods since they can learn more complex spatial and spectral patterns on visual data. This paper provides a comprehensive review of ResNet, ResNet-50, ResNet-101, and ResNet-152 in disease detection in various crops, including tomato, maize, rice, and wheat.

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.

Download article (PDF)

TY  - CONF
AU  - Priyanka Gupta
PY  - 2026
DA  - 2026/06/25
TI  - Deep Learning-Based Crop Disease Detection: A Comprehensive Review of ResNet Architectures
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 366
EP  - 375
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_27
DO  - 10.2991/978-94-6239-713-2_27
ID  - Gupta2026
ER  -