Deep learning model BiFPN-YOLOv8m for tree counting in mango orchards using satellite remote sensing data​.

Abstract

Mango is a fruit of great economic importance in India. India is the top mango-producing nation in the world, accounting for over half of global mango output. In order to determine the production capability of the insured orchards, a complete inventory is carried out in situ every three years. The inventory includes counting number of trees, grouping them into yield categories, and assessing damaged ones. Satellite Remote Sensing proves to be a vital tool for estimating ecological parameters such as population density, tree health, volume, biomass, and carbon sequestration rates. The significance of tree counting extends beyond orchard evaluations, playing a vital role in environmental protection, agricultural planning, and crop yield forecast. unfortunately, conventional tree counting methods often require very expensive feature engineering, which leads to more errors as well as lower overall optimization. In order to overcome these obstacles, deep learning-based methods have been used to count trees, exhibiting cutting-edge results in this crucial activity. This paper introduces a novel approach employing deep learning for Image-Based Mango Tree counting in high-resolution satellite imagery data. The proposed model, named Bi-directional Feature Pyramid Network (BiFPN)-YOLOv8m an improved version of YOLOv8, employs object detection to effectively separate, locate, and count mango trees with in orchards. A dataset of 1700 training and 300 testing images of mango orchards with trees of various ages is used to evaluate the various YOLOv8 variants, YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, including YOLOv9, YOLOv10, and BiFPN-YOLOv8m, with a focus on computational efficiency, accuracy, and speed. Experimental findings show that, even under difficult circumstances, the proposed method continuously outperforms state-of-the-art techniques.

Plant phenotyping relevance

衛星画像からマンゴー樹木を分離・位置推定・計数する深層学習手法を開発・評価しており、植物個体数という観測可能な形態・構造形質の抽出が中心である。

Code and data availability

The paper's satellite remote sensing image dataset used for mango tree counting is publicly deposited on GitHub per the Data Availability Statement. No separate analysis code or trained model checkpoint is explicitly deposited.