A Deep Learning for Early Tomato Leaf Disease Detection: A CNN Approach

This article introduces automated tomato leaf disease detection using Convolutional Neural Networks (CNNs). The work trains a CNN-based model to reliably distinguish healthy and sick tomato leaves using 2,000 photos. The model's capacity to recognize modest visual cues from diverse illnesses suggests it might be a viable early disease detection tool. The experimental findings show 96 % accuracy on the test dataset. CNN disease identification is a major step towards sustainable crop management. The model's ability to speed up disease diagnosis, intervene when needed, and reduce crop losses suggests it might boost crop yields and agricultural resilience. These results apply to many agricultural fields outside tomato growing. In conclusion, deep learning in agriculture may bridge the gap between traditional practices and technology innovation, increasing crop productivity and agricultural system resilience. This research promotes a future where technology and sustainable agriculture work together to develop precision agriculture.