MangoUAV-Video Dataset

While vision-based yield estimation systems are widely used for horticulture crops in recent days, accurately predicting yield in unstructured orchards remains a challenge. Automated UAV-based aerial data collection offers a scalable solution for such environments. This study utilizes UAVs to collect data from a target mango orchard, marking Points of Interest (PoIs) to ensure periodic video capture along the same path, maximizing coverage. A significant challenge addressed in this work is the detection of small mangoes in UAV-captured videos. While YOLO models, such as the latest YOLOv8, excel in object detection, these struggle with small objects in aerial imagery. To deal with this, the YOLOv8 model is customized with several enhancements, such as integration of the Efficient Sub-Pixel Convolution Network (ESPCN) to improve the spatial resolution of the feature maps and incorporation of Attention-based EfficientNet-B7 in the backbone which improves the feature extraction. Furthermore, the Bidirectional Feature Pyramid Network (BiFPN) in the neck network ensures multi-scale feature fusion for detecting objects of varying sizes. It also integrates Geometrical-IoU (GIoU) to optimize anchor box accuracy and improve object localization. Due to its high efficacy in accurately detecting mangoes in aerial images, the proposed model is named as Aerial-MangoYOLOv8. In addition to this, an ORB feature detector is used for tracking the detected mangoes to predict the yield. Rigorous experimental analysis demonstrates the superiority of Aerial-MangoYOLOv8 model over other YOLO variants and state-of-the-art detection models.