Alfalfa (Medicago sativa L.) crop vigor and yield characterization using high-resolution aerial multispectral and thermal infrared imaging technique

Abstract

Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide for animal feed, green manure, and as a land cover. However, very few approaches exist for timely field scale mapping of crop status, yield and quality attributes for management of inputs, harvest and storage resources, budgeting, crop insurance, etc. This study aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers. Imaged were two crop cutting cycles in 2018 season. Eight crop vigor index (VI) and a Crop Water Stress Index (CWSI) features were derived from collected imagery data. Modified Non-Linear Index (MNLI), Modified Simple Ratio (MSR) and CWSI reliably evaluated the spatial variations in crop vigor and stress traits (Coefficient of variation [CV] in the ranges of 24–69%). Yield was then predicted with indices as predictor variables through nine simple linear regression (LRs, variable: one image feature per model), seven multiple linear regression (MLRs, variables: one VI and CWSI per model), a stepwise linear regression (SLR), a partial least square regression (PLSR) and a least absolute shrinkage and selection operator (LASSO) models. The SLR, PLSR and LASSO initially used all image features for model training. Amongst simple models, MLR-4 (Variables: MNLI and CWSI) performed the best (Root mean square error [RMSE] = 0.45 kg, R² = 0.64) and LR-5 (Variable: MNLI) was the second-best model (RMSE = 0.51 kg, R² = 0.54). The complex SLR, PLSR and LASSO models predicted yield with similar accuracy as MLR-4 (RMSE in the ranges of 0.45–0.46 kg, R² in the ranges of 0.63–0.64). MNLI (canopy vigor) and CWSI (stress) were significant and sufficient for effective alfalfa crop status and yield prediction for their non-saturation and non-linearity features. Overall, high-resolution aerial remote sensing in the visible-NIR and thermal infrared domain showed potential for site-specific crop monitoring.

Plant phenotyping relevance

高解像度航空マルチスペクトル・熱赤外画像から作物活力、ストレス、収量を抽出・予測するセンシングおよび解析手法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。

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