A Transfer Learning Approach for Mango Leaf Disease Classification Using Convolutional Neural Networks

Pests that live on mango leaves are bad for both the safety of our food and the growth of our business. It is most common for powdery mildery, golmachi, bacterial canker, and anthracnose to be found on mango leaves in Bangladesh. If these illnesses are caught early, they can make people more productive. However, this can't be done quickly and correctly by hand. This study suggests a method that uses CNN and transfer learning to find infections before they can spread. The data set came from the Mango farm and several fields. Our goal is to quickly and accurately find mango diseases so that they can help the growing business and save farmers a lot of time and work. It was ResNet50, MobileNetV2, DenseNet201, MobileNetV3, and VGG16 that we used as transfer learning models. We used different activation functions, like elu and ReLu, as well as sigmoid and softmax functions for the thick layer to sort the model into groups. Based on the f1 Score, accuracy, memory, and precision, the suggested way is a better model than the current models. Among this model, the ResNet50 model provided better results, with 96.49% accuracy.