Coconut Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data

TL;DR

This paper demonstrates how deep learning, specifically YOLOv7, combined with synthetic training data, can accurately count coconut palm trees in drone footage, significantly improving detection precision for agricultural management.

Contribution

It introduces a semi-automated framework using YOLOv7 with synthetic images for effective coconut palm detection in drone imagery, addressing data scarcity issues.

Findings

Synthetic images improved YOLO's mAP from 0.65 to 0.88.

Optimal drone altitude enhances detection accuracy.

Synthetic training data is valuable for agricultural object detection.

Abstract

Drones have revolutionized various domains, including agriculture. Recent advances in deep learning have propelled among other things object detection in computer vision. This study utilized YOLO, a real-time object detector, to identify and count coconut palm trees in Ghanaian farm drone footage. The farm presented has lost track of its trees due to different planting phases. While manual counting would be very tedious and error-prone, accurately determining the number of trees is crucial for efficient planning and management of agricultural processes, especially for optimizing yields and predicting production. We assessed YOLO for palm detection within a semi-automated framework, evaluated accuracy augmentations, and pondered its potential for farmers. Data was captured in September 2022 via drones. To optimize YOLO with scarce data, synthetic images were created for model training and validation. The YOLOv7 model, pretrained on the COCO dataset (excluding coconut palms), was adapted using tailored data. Trees from footage were repositioned on synthetic images, with testing on distinct authentic images. In our experiments, we adjusted hyperparameters, improving YOLO's mean average precision (mAP). We also tested various altitudes to determine the best drone height. From an initial mAP@.5 of $0.65$, we achieved 0.88, highlighting the value of synthetic images in agricultural scenarios.