Approach of Dynamic Tracking and Counting for Obscured Citrus in Smart Orchard Based on Machine Vision

Abstract

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards.

Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing

“…Yang et al [7] used Mask R-CNN and branch segment merging algorithms to detect citrus fruits and branches in the presence of foliage occlusion and mapped color images onto depth images to obtain the diameters of fruits and branches. Feng et al [8] used the lightweight target-detection network YOLOv7-tiny on citrus in video technique, which can measure the citrus yield in an orchard. Cong et al [9] introduced RGB-D images and used MSEU R-CNN to segment citrus canopies to obtain the growth information of the canopies, which provided a method for spraying robots.…”