Assessing Drone-Based Remote Sensing Indices for Monitoring Rice Nitrogen Plant Status Under Different Irrigation Techniques

Abstract

The rice sector is facing the challenge of increasing rice yields while maintaining or improving input use efficiency. The purpose of this study was to determine the most effective vegetation indices for monitoring nitrogen uptake (N uptake) under different irrigation techniques. The study was conducted in Uruguay over two rice-growing seasons. A split plot experimental design featured two irrigation treatments (main plots): continuous flooding (C) and alternate wetting and drying (AWD). The nitrogen-rate (N-rate) treatments (split plots) included no nitrogen, the recommended N-rate based on soil analyses, and two additional doses (±50% of the recommendation). The plant N uptake relationships with selected drone-based vegetation indices (VIs) were assessed at panicle initiation. The presence or absence of standing water during image collection affected the VIs and their relationships with N uptake. The relationships estimated for traditional irrigation may not be applicable for AWD. The SCCCI was the top index with a significantly stronger relationship with N uptake under the C (R2 = 0.84) and AWD (R2 = 0.71) irrigation techniques in relation to all evaluated vegetation indices. The Clre, NDRE2, NDRE, and CLg also had a significant relationship with N uptake under both irrigation treatments in both seasons, though their average R2 values of 0.75, 0.74, 0.73, and 0.71, respectively, were lower than the SCCCI (average R2 = 0.78). The findings would assist rice growers for selecting effective VIs for remote crop monitoring.

Plant phenotyping relevance

ドローン画像由来の植生指数を比較・評価し、イネの窒素吸収量という植物生理状態の推定における有効性を検証しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe drone multispectral imagery, VI computation, and R-based statistical analysis, but contain no data availability statement, no deposited phenotype dataset, no public imagery, and no author code repository or URL. All URLs in the text are references, software homepages, or institutional links

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