A Novel Fusion Network for Apple Image Classification and Quantity Recognition

We focuses on identifying images containing apples from a large number of orchard fruit images and determining the number of apples in each filtered image. We propose a CV5Fnet model that combines traditional OpenCV image processing operations with the Watershed Algorithm and YOLO V5. We first build a high-precision, lightweight fruit classifier to accurately filter apple images from five fruit images in the dataset, and pass apple images to the red apple recognition module and the green apple recognition module based on YOLOV5, which are based on the filters, HSV color space conversion, masking operations, and Watershed Algorithm, respectively. The apple pictures are passed to the red apple recognition module based on filter, HSV color space conversion, mask operation, watershed algorithm and the green apple recognition module based on YOLOV5 to recognize the number of red apples and the number of green apples in the target pictures. and green apples in the target picture respectively, and finally sum up to the number of apples in the picture. In the publicly available dataset 2023APMCM_A_2, the accuracy of our fruit classifier is as high as 99.86%, and the final image processing results show that CV5Fnet has achieved good results in recognizing the number of apples.