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

CONTEXT: 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. OBJECTIVE: To tackle the above issues, this paper proposed a robust and efficient fruit-counting pipeline based on Unmanned Aerial Vehicle (UAV). METHODS: 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. RESULTS AND CONCLUSIONS: The results indicate that for detection performance, the P, R and mAP of the FruitYOLO 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. SIGNIFICANCE: 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.