An Enhanced Wheat Leaf Disease Classification Using Pre-Trained InceptionV3 Model

Wheat is widely recognized for having a high nutritional content since it includes essential proteins, fiber, carbs, and vitamins and minerals. This crop is a crucial energy source for both humans and other creatures. Food production is also affected by various diseases. After providing an extensive overview of the categorization of wheat leaf diseases, this paper highlights the need for early and accurate identification to protect wheat output globally. Spectral analysis, machine learning, and image processing advances have made substantial progress in automating the classification of wheat leaf disease. a summary of the accuracy and loss throughout several training and validation epochs. This Inception V3 model applies different epochs such as 15, 20, 25, and 30. Empirical evidence suggests that the model performs optimally around epoch30, which also has the lowest loss (0.2513 in the training phase and 0.2069 in the validation phase) and the highest accuracy (0.9075 in the training phase and 0.9218 in the validation phase).