Authors - Md. Nadimul Islam, Sajid-Ul Islam, Tahsina Islam Afra, Mohammad Shidujaman Abstract - Foliar diseases impact negatively on the health and productivity of mango trees, hence it is essential to manage them effectively. The proposed research is an automated approach to diagnosing popular in common mango leaf diseases, such as Anthracnose, Bacterial Canker, and Powdery Mildew, utilizing high-throughput imagery. The suggested methodology deploys a Transfer Learning model which employs MobileNetV2 framework which is already trained using ImageNet to guarantee successful and precise classification on battery limited devices such as Raspberry Pi. With the combination of target feature detection and a specialized classification head, the system offers real-time detection that can be used in spraying mechanisms using the IoT. Through experimental analysis, it is shown that the proposed CNN-based framework is highly accurate in terms of classification when the experiment is conducted under controlled conditions and as such, the framework has potential to be used in automated mango leaf disease detection.
