Overall accuracy alone is insufficient for judging the reliability of multi-class plant disease detection systems, and practical deployment of CNN-based tomato disease detection therefore requires improved class balance, stronger validation, and attention to computational performance.
A comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification demonstrates that transfer learning can effectively improve classification performance in plant disease recognition tasks.
Sumana Budsabok, Wachiraporn Polpanumas, Piyanan Khongphai· International Journal of Ele...· 0 citations
Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
The proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.
Sonali Kamra, Vijay Laxmi· International Journal of Res...· 0 citations
The study demonstrates that transfer learning effectively enhances plant disease classification, enabling faster, more reliable and resource efficient detection for precision agriculture.
Chika K. Gangadharan, P. M. Jasmine, Roshni Alex et al.· Indian Journal of Agricultur...· 0 citations
This study presents a novel CNN for multi-class classification of 38 diseases, demonstrating an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 0 citations
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