Deep Learning Based Pepper Maturity Detection

China is one of the world's important pepper production area, the existing picking method still rely on manual picking method for picking, and pepper is a kind of batch maturity of the herb, manual picking is not only time-consuming but also very laborious, and the pepper target detection technology is the key to realize the automatic picking of pepper, the existing research for pepper target detection is relatively small, this paper intends to choose the image processing and machine learning methods for pepper to carry out target detection. First of all, the processed pepper image data are passed into Yolov5s, Yolov5m, Faster R-CNN, SSD to compare the four common deep learning methods, the comprehensive comparison in the pepper recognition stage Yolov5s is the most appropriate deep learning algorithm in the deep learning algorithms mentioned above, and in the second stage, using the binary classification method, the pepper data are divided into immature pepper and mature pepper, and directly sent to Yolov5s. The second stage uses a binary classification method to classify pepper data into immature and mature pepper, which is directly fed into the Yolov5s model with an accuracy of 97.4% for pepper maturity discrimination, providing diversified detection and corresponding technical support for the intelligent pepper picking summary.