Research on Apple Recognition and Counting Model Based on Image Processing

With the intensification of the shortage of agricultural labor, especially in the picking season, traditional manual picking has been difficult to meet the demand. To address this challenge, China has made significant progress in the research of automated apple picking robots since 2011. Nevertheless, the widespread application of apple picking robots in complex orchard environments still faces many difficulties, such as inaccurate identification of obstacles such as “leaf shade”, “branch shade” and “fruit shade”. These problems have led to reduced harvesting efficiency and even increased fruit damage and personnel safety risks. In order to improve the picking accuracy of the robot, this paper extracts the image features including color, shape and texture based on the provided apple image data set, and establishes a mathematical model that can accurately identify and count apples. The model adopts many image processing techniques such as color space conversion, color filtering and contour detection to improve the adaptability and operation speed in complex environment. In addition, the generalization ability of the model is key to ensuring that it can be accurately identified and counted across different orchards and diverse apple appearances. By analyzing and processing these image data, this paper draws a histogram of the distribution of all apples, clearly showing the spatial distribution of apples in the orchard, and provides valuable data support for future automated picking and orchard management. This research not only helps to solve the problem of labor shortage, but also provides a technical basis for improving the efficiency of apple picking and fruit quality.