UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

Abstract

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

Plant phenotyping relevance

UAVマルチスペクトル画像と深層学習を用いた柑橘の表現型取得・解析手法を開発し、樹冠サイズ、樹木状態、欠損木などを大規模に評価しており、表現型測定法が研究の中心である。

Code and data availability

The article describes UAV multispectral imagery, a YOLOv3 training set (~400 images, ~15,000 labeled trees), and phenotypic analysis code, but contains no public deposit, availability statement, or authors' URL for any dataset, imagery, model, or script. The only URL present is the CC BY license notice, which is not a

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