Detecting spatial variation in wild blueberry water stress using UAV-borne thermal imagery: distinct temporal and reference temperature effects

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2025-01-28 DOI:10.1007/s11119-024-10216-y

Kallol Barai, Matthew Wallhead, Bruce Hall, Parinaz Rahimzadeh-Bajgiran, Jose Meireles, Ittai Herrmann, Yong-Jiang Zhang

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引用次数: 0

Abstract

The use of thermal-based crop water stress index (CWSI) has been studied in many crops in semi-arid regions and found as an effective method in detecting real-time crop water status of commercial fields remotely and non-destructively. However, to our knowledge, no previous studies have validated the usefulness of CWSI in a temperate crop like wild blueberries. Additionally, the temporal changes of the water status estimation model has not been well-studied. In this multi-year study, Unoccupied Aerial Vehicle (UAV)-borne thermal imageries were collected in 2019, 2020, and 2021 to test the temporal effects and the impact of different approach-based reference temperatures (T_wet, wet reference temperature; T_dry, dry reference temperature) on leaf water potential (LWP) estimation models using CWSI in two large adjacent wild blueberry fields in Maine, United States. We found that different sampling dates have a significant impact on LWP estimation models using CWSI_SE (statistical T_wet and empirical T_dry reference) and CWSI_SS (statistical T_wet and statistical T_dry reference). Further, CWSI_BB calculated with bio-indicator-based T_wet and T_dry reference was found more effective (r² = 0.79) in estimating LWP in 2021, compared to the CWSI_SE and CWSI_SS approaches in 2019 (r² = 0.34 & r² = 0.36), 2020 (r² = 0.38 & r² = 0.44) and 2021 (r² = 0.43 & r² = 0.46). CWSI_BB -LWP model-based crop water status maps show high variation in the crop water status of wild blueberries, even in an evenly irrigated field, suggesting the potential of UAV-borne thermal cameras to detect real-time crop water status within the field, with the CWSI_BB calculated from bio-indicator-based references being more reliable. Our results could be used for precision irrigation to increase the overall water use efficiency and profitability of wild blueberry production.

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利用无人机热成像检测野生蓝莓水分胁迫的空间变化：不同的时间和参考温度效应

利用基于热的作物水分胁迫指数（CWSI）对半干旱区的许多作物进行了研究，发现它是一种远程、非破坏性地实时检测商品田作物水分状况的有效方法。然而，据我们所知，以前没有研究证实CWSI在温带作物如野生蓝莓中的有用性。此外，水势估算模型的时间变化研究还不够深入。在这项为期多年的研究中，研究人员在2019年、2020年和2021年收集了无人驾驶飞行器（UAV）机载热图像，以测试基于方法的不同参考温度(Twet，湿参考温度；在美国缅因州两个相邻的大型野生蓝莓田中，利用CWSI对叶片水势（LWP）估算模型的影响。研究发现，不同的采样日期对CWSISE（统计Twet和经验Tdry参考）和cwiss（统计Twet和统计Tdry参考）的LWP估计模型有显著影响。此外，与2019年的CWSISE和CWSISS方法相比，基于生物指标的Twet和Tdry参考计算的CWSIBB在估计2021年的LWP方面更有效（r²= 0.79）(r²= 0.34 &；R²= 0.36),2020 (R²= 0.38 &；R²= 0.44)和2021 (R²= 0.43 &；R²= 0.46)。基于CWSIBB -LWP模型的作物水分状况图显示，即使在均匀灌溉的田地中，野生蓝莓的作物水分状况也存在很大变化，这表明无人机热像仪在检测田间实时作物水分状况方面具有潜力，而基于生物指标的参考计算的CWSIBB更为可靠。我们的研究结果可用于精确灌溉，以提高野生蓝莓生产的整体用水效率和盈利能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.