AI-Driven Django Framework for Multi Crop Disease Classification for Precise Agriculture
Keywords:
Keywords: Crop disease detection, Deep learning, Convolutional Neural Network (CNN), Django framework, Real-time diagnosis, Precision agriculture.Abstract
In modern agriculture, the ability to accurately identify and classify crop diseases is crucial forimproving crop yield and ensuring food security. Traditional methods of disease diagnosis rely heavilyon manual inspection, which is time-consuming and prone to human error. With the advancement ofartificial intelligence (AI), particularly deep learning models, there is a significant opportunity toautomate and improve the disease classification process. This paper proposes an AI-driven system basedon the Django framework for multi-crop disease classification
References
Jafar, A., Bibi, N., Naqvi, R.A., Sadeghi-Niaraki, A. and Jeong, D., 2024. Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Frontiers in Plant Science, 15,p.1356260.
Demilie, W.B., 2024. Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data, 11(1), p.5.
