Use of High Resolution Unmanned Aerial Systems Imagery and Machine Learning to Evaluate Grain Sorghum Tolerance to Mesotrione

Abstract

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Plant phenotyping relevance

UAS画像、植生指数、機械学習分類を用いてソルガムの除草剤傷害を評価する手法を比較・検証しており、植物状態の取得・推定が研究の中心です。

Code and data availability

The supplied blocks describe UAS multispectral imagery collection, image classification, and R-based statistical analysis for sorghum mesotrione injury, but contain no public phenotype dataset, imagery deposit, author code, or trained model. All listed URLs are vendor pages (MicaSense, R, ArcGIS) or cited references (P

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