A Pre-trained MobileNetV2 Model for Multiclass Sunflower Leaf Disease Classification

Sunflowers are used in agriculture, but they also have cultural and symbolic meaning as representations of concepts like energy, resilience, and optimism. As one of the most important oilseed crops in the world, sunflowers are prone to several leaf diseases that can seriously affect both quality and productivity. The use of image processing and classification techniques to classify diseases that impact sunflower leaves is a significant advancement in agricultural research and disease prevention strategies. This article classifies diseases of sunflower leaves using the MobileNetV2 model. To get the most out of the model, four different epochs are used. At epoch 30, the model ultimately achieved the highest accuracy and lowest loss. The data indicates that Epoch 30 has the best accuracy (0.9218 in the validation phase and 0.9075 in the training phase) and the lowest loss (0.2513 in the training phase and 0.2069 in the validation phase).