Heading and maturity date prediction using vegetation indices: A case study using bread wheat, barley and oat crops

IF 4.5 1区 农林科学 Q1 AGRONOMY European Journal of Agronomy Pub Date : 2024-08-31 DOI:10.1016/j.eja.2024.127330
Adrian Gracia Romero, Marta S. Lopes
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Abstract

Contemporary crop research programs involve the evaluation of numerous micro-plots spread across extensive experimental fields. As a result, there is a growing need to depart from labor-intensive manual measurements when assessing phenological data. The growing significance of high throughput phenotyping platforms (HTTP), including unmanned aerial vehicles (UAVs), has rendered these technologies essential in crop research. The overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions. To pinpoint DH and DM prediction, a total of nine UAV surveys were conducted throughout the entire crop cycle. Prediction models for DH and DM using VIs were successfully developed for various crop species, explaining 65 % of the variance in bread wheat and 75 % in oats. The highest percentages of variance explained were achieved when models were developed separately for the two irrigation conditions (well-irrigated and rainfed). However, the percentage of variance explained by these models decreased when applied to barley (R²<0.5 for DH). Notably, including final plant height as a predictor increased the percentage of variance explained by the models only for irrigated bread wheat. Furthermore, the utilization of multi-temporal equations, which amalgamated data from diverse UAV surveys, notably enhanced the percentage of variance explained by the model (+160.71 % improvement in DH predictions), particularly those tailored to each specific crop species and irrigation condition. The investigation additionally established a thorough protocol for modeling the phenological aspects of cereal crops utilizing data acquired from UAVs, thereby enhancing the accessibility of this technology for measurements of phenology in large crop research programs.

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利用植被指数预测发情期和成熟期:利用面包小麦、大麦和燕麦作物进行案例研究
当代作物研究项目涉及对分布在大面积试验田中的大量微型地块进行评估。因此,在评估表型数据时,越来越需要摆脱劳动密集型的人工测量。高通量表型平台(HTTP),包括无人飞行器(UAV)的重要性与日俱增,使这些技术在作物研究中变得至关重要。本研究的总体目标是探索和验证 HTTP 方法的使用,特别是由传统 RGB 图像得出的植被指数(VIs)的潜力,以预测不同灌溉条件下各种谷类作物的发棵期(DH)和成熟期(DM)。为了准确预测打顶期和成熟期,在整个作物周期共进行了九次无人机调查。针对不同作物品种,成功开发出了使用 VIs 的 DH 和 DM 预测模型,对面包小麦和燕麦的方差解释率分别为 65% 和 75%。当针对两种灌溉条件(良好灌溉和雨水灌溉)分别建立模型时,解释的变异百分比最高。然而,当这些模型应用于大麦时,其解释的变异百分比有所下降(DH 的 R²<0.5)。值得注意的是,将最终株高作为一个预测因子,只提高了灌溉面包小麦模型解释的变异百分比。此外,利用多时空方程(将来自不同无人机调查的数据合并在一起)显著提高了模型解释的方差百分比(DH 预测值提高了 +160.71%),特别是那些针对每种特定作物和灌溉条件的模型。这项调查还为利用无人机获取的数据建立谷类作物物候建模建立了一套完整的规程,从而提高了这项技术在大型作物研究项目中用于物候测量的便利性。
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来源期刊
European Journal of Agronomy
European Journal of Agronomy 农林科学-农艺学
CiteScore
8.30
自引率
7.70%
发文量
187
审稿时长
4.5 months
期刊介绍: The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics: crop physiology crop production and management including irrigation, fertilization and soil management agroclimatology and modelling plant-soil relationships crop quality and post-harvest physiology farming and cropping systems agroecosystems and the environment crop-weed interactions and management organic farming horticultural crops papers from the European Society for Agronomy bi-annual meetings In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.
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