A Canopy Height Model Derived from Unmanned Aerial System Imagery Provides Late-Season Weed Detection and Explains Variation in Crop Yield

Abstract

Weeds pose a ubiquitous challenge to researchers as a source of unintended variation on crop yield and other metrics in designed experiments, creating a need for practical and spatially comprehensive techniques for weed detection. To that end, imagery acquired using unmanned aerial systems (UASs) and classified using pixel-based, object-based, or neural network-based approaches provides researchers a promising avenue. However, in scenarios where spectral differences cannot be used to distinguish between crop and weed foliage, where physical overlap between crop and weed foliage obstructs object-based detection, or where large datasets are not available to train neural networks, alternative methods may be required. For instances where there is a consistent difference in height between crop and weed plants, a mask can be applied to a canopy height model (CHM) such that pixels are determined to be weed or non-weed based on height alone. The CHM Mask (CHMM) approach, which produces a measure of weed area coverage using UAS-acquired, red–green–blue imagery, was used to detect Palmer amaranth in Sweetpotato with an overall accuracy of 86% as well as explain significant variation in sweetpotato yield (p

Plant phenotyping relevance

UAS画像から作物キャノピー高モデルを構築し、雑草面積被覆率という植物状態を推定するCHMM手法が中心で、精度評価も行っているため収載する。

Code and data availability

The paper's phenotyping data (UAS RGB imagery, DSM/DTM/CHM rasters, CHMM weed-area measurements, visual weediness ratings, and sweetpotato yield data) are not deposited in any public repository; the Data Availability Statement states they are available only upon request from the corresponding author. The Supplementary

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