Real Life Image Recognition of Panama Disease by an Effective Deep Learning Approach

Because of the rapid development of information technology, the deep learning for numerous applications is a fairly popular and hot research issue currently. Deep learning, as one of the most currently extraordinary machine learning methods, has obtained substantial success in considerable applications such as image analysis, speech recognition and text understanding. It uses supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for the tasks of classification and image recognition. This research is concerned with a real life image recognition for panama (banana) disease which optimizes the performance of deep learning techniques. This study is based on a deep learning technique called MResNet (modified ResNet) and modify activation function to enhance accuracy, precision and recall. According to the experimental results, the proposed approach is fairly effective to detect panama disease.