<![CDATA[The agriculture sector plays a significant role in Morocco's economy, and tomato farming is an essential component of this industry. However, tomato plants are prone to various diseases that can adversely affect productivity and quality. A novel approach to detect tomato plant diseases is proposed int this study, by modeling and developing a transfer learning-based convolution neural network (CNN) model that processes real-time images. The model is trained and validated with a deep CNN using a private dataset of 18,159 annotated tomato leaf images collected from experimental farms over five months. The performance of our residual neural network (ResNet-50) model is evaluated using stochastic gradient descent (SGD) and adaptive moment estimation (Adam) optimizers to demonstrate superior efficiency. Farmers can simply send images of their tomato leaves through our platform, and the trained model will identify accurately the disease. The developed model demonstrates exceptional performance, achieving a 0.96 F1 score and an 97% accuracy rate when tested on a dataset generated from real-world fields. This approach not only improves disease detection but also contributes to sustainable farming practices and enhanced productivity.]]>
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