Recognition of Apple Leaves Infection Using DenseNet121 with Additional Layers

Apple is one of the most popular fruits all over the world, and it is also very liable to diseases like scabs, apple rot, and leaf blotch. These diseases majorly destroy the quality and lead to less healthy production; it is difficult to identify diseases in apples as they appear for a short interval of time so the most prominent way to identify infection is through the condition of their leaves. Most of the leaves are infected by scab, rust, bacteria, and viruses. Early detection is complex for farmers as they all appear the same in shape, color, and texture. Deep learning technology is contributing greatly in this area, addressing this we have proposed an accurately improved Segmentation with the CNN model using a transfer learning model, i.e., DenseNet121 with the weight of ImageNet, and by adding an extra top layer for accurate results. This study also includes the comparative analysis of Seg+ DenseNet121 and some integrated machine learning models. This experiment achieves 99.06% accuracy.