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 multilevel 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.

A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a realtime crop diseases and pests video detection system in the future, a deep learningbased video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the stillimage detector for detection, and finally synthesized the frames into video. In the stillimage detector, we used fasterRCNN as the framework. We used imagetraining models to detect relatively blurry videos. Additionally, a set of videobased evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet50, ResNet101 backbone system and YOLOv3 with our experimental environment.