Research and experiment on pepper recognition based on improved convolutional neural network algorithm

Pepper is a common vegetable, with a wide range of applications and market demand. Pepper recognition is an important task when harvesting peppers by a robot. Due to the fact that chili peppers are usually planted very densely, the fruits often grow in clusters. As a result, identifying and locating peppers that are obscured by other peppers, branches, and leaves becomes a challenging task. In this task, the robot needs to overcome visual obstacles in order to accurately detect and locate the target pepper. This paper proposes a pepper recognition method based on the improved You Only Look Once version 3 (YOLOv3) convolutional neural network. By adding Res2Net backbone into the feature extraction network of YOLOv3, the extraction performance is improved, and the accuracy and efficiency of pepper identification are also improved. In this study, large-scale pepper image data sets were used for training and testing, and the generalizability and robustness of the model were improved through data enhancement and transfer learning techniques. For the test samples, the recognition rate of the traditional Backpropagation Neural Network (BPNN) algorithm is 66.25%, while the recognition rate of the improved YOLOv3 convolutional network for the same test samples is 91.25%. The experimental results show that this method has good performance in the pepper recognition task, with high accuracy and fast processing speed.