Potato Leaf Disease Detection and Classification Using VGG16

Agriculture is a critical sector that sustains human life by providing food, fiber, and raw materials. The challenges faced by agriculture today such as population growth, climate change, and resource constraints make us to ensure food security and environmental sustainability. With digitalization becoming widespread in various industries, it has become easier to accomplish difficult tasks. The agriculture sector needs to incorporate technology and digitalization to reap benefits for both farmers and customers. By using technology and regular monitoring, it is possible to detect illnesses in crops early on, remove them, and boost crop yield. The article introduces a proposed system designed to identify and classify diseases that affect potato plants. In order to classify the diseases, we have used the non-restricted dataset from Kaggle that is PlantVillage dataset. The image processing technique is used to detect the different regions of the leaf. VGG16 is employed for training and classifying the dataset.