Aug 2026· International Conference on Information Security and Cryptology· pp. 1392-1396· 0 citations· 18 references
Abstract
Many leaf diseases have significant effects on yield and quality and wheat is an important crop contributing to food security globally. Accurate and timely diagnosis of these diseases is important for the sustainable use of agriculture. This study assesses the effectiveness of deep learning (DL) technique using ResNet for wheat leaves identification and classification. The proposed architecture aims to address the drawbacks of the conventional Convolutional Neural Networks (CNNs) by using ResNet50, ResNet101 and ResNet152. A variety of parameters are used to assess prediction models on benchmark datasets. The research suggests that ResNet50 beats other classes of ResNets with a classification accuracy of 90%. Using improved learning rates and optimized feature extraction layers, the network is able to successfully differentiate between healthy and unhealthy wheat leaves. For agricultural monitoring systems, this research reveals that ResNet models have great potential to accurately identify diseases in real-time.
The occurrence of diseases in rice leaves is a crucial problem for agricultural productivity and food security, requiring the development of accurate algorithms to reliably detect these conditions. Although deep learning methods, especially Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have alrea...
D. Rohan, G. P. Reddy, K. P. Prakash· 2026 IEEE International Conf...· 0 citations
: Tomatoes are among the most important horticultural crops for the processing industry worldwide and play a key role in human diets due to their high nutritional and culinary value. However, plant diseases have become a major threat to global food security, increasing the need for reliable and early detection methods....
Zahraa Mohammed Taha Issa, H. Najm· Wasit Journal for Pure scien...· 0 citations
This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.
Yu-Yan Xu, H. Chen, Qing-Mei Lin· Legume Research An Internati...· 0 citations
A comprehensive and systematic review of state-of-the-art methods for detecting potato leaf disease, covering convolutional neural networks, transformer-based architectures, and hybrid models, and a strategic comparative analysis is conducted.
Rice leaf diseases have a great impact on the productivity and food security of crops. Thus, there is a necessity for development of accurate and fast automatic rice leaf disease detection. In the current paper, we propose an optimized deep learning model for multi-class rice leaf disease detection through transfer lea...
Ankush Jariyal, Anmol Goyal· 2026 International Conferenc...· 0 citations
The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.
W. Zalmi, Rahmi Putri Kurnia, Dyah Listianing Tyas· Informatik : Jurnal Ilmu Kom...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
