Detection of Wheat Leaf Disease Using Transfer Learning: A Comparative Study

Wheat (Triticum aestivum) is a vital staple crop that provides many people with food security and a means of subsistence worldwide. However, pests, illnesses, and environmental stressors threaten wheat agriculture, endangering crop yields and farmer incomes. Sustainable agriculture depends on promptly identifying and treating illnesses affecting wheat plants. Automated disease identification could be improved with the help of convolutional neural networks (CNNs), one of the most recent developments in deep learning. This essay clarifies the importance of wheat plants in international agriculture, emphasizing the need for disease control. It highlights how revolutionary wheat plant disease detection might be using DenseNet201, MobileNetV2, Xception, VGG-19, VGG-16, and InceptionV3. Through the application of transfer learning techniques, these models show remarkable precision in the…