Recognition and Classification of Deep Learning in Soybean Leaf Image Data Management - LU Lina, YU Xiao (Nongye tushu qingbao xuebao, 2023)
[Purpose/Significance] We used to process soybean leaf data by looking at them and process data manually, but this method is very inefficient. In order to improve the classification accuracy and efficiency of soybean leaf images, further for storage and management of these images, we used the deep learning technique to make an in-depth study of text data and image data of soybean leaves for the image recognition and classification. The application of deep learning in agricultural data management mainly focuses on the image recognition and classification of plants and plant phenotypes in large-scale data, detection and classification of agricultural diseases and pests, detection and classification of crops and weeds, and prediction of crop yield. Through case analysis, our sample data demonstrated the application process of deep learning technology. [Method/Process] This paper systematically described the whole process of classification and recognition of agricultural data by using the deep learning technique. Through recognition and disease monitoring of plant leaves, the leaf morphology of soybean plants in the soybean experimental field of Heilongjiang Academy of Agricultural Sciences was taken as an example. We analyzed the image features of soybean leaf morphology, and carried out the classification and recognition research of soybean leaf morphology based on deep learning. Deep learning techniques have replaced shallow classifiers that use manual feature training and can identify soybean leaves with a high degree of accuracy as long as sufficient data are available for training. We adopted DenseNet model, which is suitable for common network model. The advantages of this model are that it has the best performance and the least storage requirements. First,we selected support vector machine (SVM) and random forest (RF) in traditional machine learning methods to identify soybean leaf morphology. Second, AlexNet and ResNet were selected to identify soybean leaf morphology. Finally, the recognition accuracy of different methods were compared with the DenseNet network adopted in this paper. [Results/Conclusions] Through the training of DenseNet model, the recognition accuracy of 94% was achieved, which successfully solved the problems of long time, low efficiency and low recognition accuracy of traditional methods in processing image classification of soybean leaves, and could meet the actual needs of agricultural image data classification. Future research efforts will strive to collect a wide range of large and diverse data sets to facilitate soybean leaf recognition, and focus on developing reliable background removal techniques and incorporating other forms of data to improve the accuracy and reliability of soybean leaf recognition systems.