Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data

Abstract

Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error

Plant phenotyping relevance

RGB UAV 3Dデータと地上ハイパースペクトル画像を用いた作物高さ・反射情報から、野菜作物の生体重収量を推定し、予測性能を検証することが研究の中心であるため。

Code and data availability

The paper describes UAV RGB point clouds, terrestrial hyperspectral imagery, and biomass measurements for vegetable crops, but no public deposit of these data, images, code, or trained models is stated. The supplementary materials only contain summary tables of height metrics and model validation performance, not the原始

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