Mapping Peach Leaf Curl Disease Severity: CNNXGBoost Hybrid Analysis

Peach leaf curl disease is a big problem for peach orchards all over the world. It needs effective ways to control and manage it properly. This study is the first of its kind to use a mix of deep learning, combining Convolutional Neural Networks (CNN) and XGBoost. This approach helps analyze different stages of peach leaf curl disease properly. A group of 12,000 pictures showing different levels of disease were carefully gathered. Then a new model was built that combines CNN to look at images and XGBoost for handling environmental information. The mixed-up way showed an amazing total correctness of 97.7% for sorting sick stages. This is important for reducing mistakes in placing things into wrong categories. Its need for environmental information also means that more data has to be added regularly. This helps make predictions better and more accurate. This study shows a strong tool for exact farming. It helps find and deal with disease early on. Peach leaf curl disease is only one example of the hybrid model's use in agriculture. This promises to deliver fruitful farming methods and helpful care for orchids that can last a long time.