A robust and efficient citrus counting approach for large-scale unstructured orchards

Accurately detecting and counting fruits is essential for orchard yield estimation and smart management. However, two drawbacks are inherent in the current citrus counting algorithms: First, the robustness needs to be improved, particularly illumination changes and dense occlusion. Secondly, the actual operating efficiency of the system is low. To tackle the above issues, this paper proposed a robust and efficient fruit-counting pipeline based on Unmanned Aerial Vehicle (UAV). First, to obtain UAV video streaming data online, a live broadcast platform and a flight control Application called FlyCounter were developed. Secondly, the Illumination-Adaptive-Transformer network is used to enhance the low-illumination citrus image in real-time. Then, for the specific challenging scenario, a novel model named Fruit-YOLO is designed to accurately detect citrus in data streams. Finally, the DeepSORT is adopted to track and count fruits in video sequences. The results indicate that for detection performance, the P, R and mAP of the Fruit-YOLO of input enhanced image are 0.898, 0.854 and 0.929 respectively, which are 1.7%, 4.6% and 3.5% higher than the original image respectively. Regarding counting performance, for the daytime and evening scenes, the ID switch, MOTA and Error Mean of the pipeline are 63.7, 0.86 and 9.81% respectively. The MAPE of the offline and online operations of the pipeline are 4.36% and 12.66% respectively. The time and resources consumed by the system under parallel operations with different numbers of UAVs were analyzed. In this study, an online counting pipeline based on UAVs that can work in low-light scenarios is implemented for the first time. The system has good performance in both daytime and nighttime scenarios, enabling efficient counting in orchards and extending operating time.