Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

Abstract

In this paper, we propose a deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images in a palm tree Farm in the Kharj region, in Riyadh Saudi Arabia, using DJI drones, and we built a dataset of around 10,000 instances of palms trees. Then, we developed a convolutional neural network model using the state-of-the-art, Faster R-CNN algorithm. Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to detect the geographical location of detected palms trees automatically. This geolocation technique was tested on two different types of drones (DJI Mavic Pro, and Phantom 4 Pro), and was assessed to provide an average geolocation accuracy of 2.8m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, it can be generalized to the geolocation of any other objects in UAV images.

Plant phenotyping relevance

航空画像からヤシ個体を自動検出・計数する画像解析手法を開発し、データセット構築と異なるドローンでの精度評価も行っており、植物個体数の取得が中心的な方法論的貢献である。

Code and data availability

The paper describes a custom UAV palm-tree dataset (217 images, 9,873 instances) and Faster R-CNN/YOLO models, but provides no public availability statement, deposit, or authors' URL for the dataset, images, code, or models. The only URL mentioned (Labelbox) is a generic commercial annotation tool, not a paper-specific

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