UAV imagery coupled deep learning approach for the development of an adaptive in-house web-based application for yield estimation in citrus orchard

Orchard yield estimation enables a farmer to make informed decisions. The limitations of visual inspection-based yield estimation approaches can be effectively addressed by the intervention of unmanned aerial vehicles (UAVs) and advanced image processing using deep learning algorithms. This study proposes a novel methodology combining a deep learning-driven UAV imagery and an in-house web-based application, "DeepYield"; to measure yield in a citrus fruit orchard. The state-of-the-art deep learning object detection models SSD, Faster RCNN, YOLOv4, YOLOv5 and YOLOv7 were evaluated for detecting "harvest-ready" and "unripe" citrus fruits from the tree images. Fruit size estimation was carried out using traditional as well as deep learning-based image segmentation models. YOLOv7 outperformed other models with a mAP, Precision, Recall, and F1-Score of 86.48, 88.54, 83.66 and 86.03%, respectively. The developed solution was integrated into a web-based application as 'DeepYield' to enhance users' convenience and equip them with an automated yield estimation solution.