Soybean Pod Morphology Classification Based on Machine Vision and Convolutional Neural Network

Abstract

Pod morphology is one of the most crucial indicators for breeding high-quality soybeans. Therefore, the accurate classification of pod morphology is pivotal. Aiming at the problem that the morphology of soybean pods cannot be accurately classified, we proposed an automatic classification method based on machine vision and convolutional neural networks in this paper. First, we designed an image acquisition system to obtain soybean pod images. Then, a convolutional neural network model was designed to extract the deep features of soybean pods in the images. Finally, the deep feature training support vector machine (SVM) classifier was used to classify soybean pods of different forms, and the traditional features are designed to compare the morphological classification of soybean pods. The classification accuracies of this method were 98.8%, 99.2%, and 98.1% for straight, curved scythe, and bowhead bean pods, respectively, with an average classification accuracy of 98.7%, and the classification accuracy of deep features is 5.5% higher than that of traditional feature extraction. Experimental results prove that the proposed automatic classification method is feasible for soybean pod morphology classification, and it also has potential in classification of other characteristics of soybean, such as leaves, grains, and diseases.