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UAV remote sensing for phenotyping drought tolerance in peanuts

机译：无人机遥感对花生表型耐旱性的研究

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Farmers can benefit from growing drought tolerant peanut (Arachis hypogaea L.) cultivars with improved yield when rainfall is sporadic. In the Virginia-Carolina (VC) region, drought is magnified by hot summers and usually occurs in July and Aug when pod and seed growth are intense. At these growth stages, weekly supply of 50 to 75 mm of water is needed to ensure profitability. Irrigation can supplement crop water needs, but only 10% of the peanut farms are irrigated. In this frame, drought tolerant varieties can be profitable, but breeding for cultivars with improved drought tolerance requires fast yet accurate phenotyping. Our objective was to evaluate the potential of UAV remote sensing technologies for drought tolerance selection in peanut. In this study, we examined the effect of drought on leaf wilting, pod yield, grading characteristics, and crop value of 23 peanut cultivars (Virginia, Runner, & Valencia type). These varieties were arranged in a factorial design, with four replications drought stressed and two replications well-watered. Drought was imposed by covering the drought stressed plots with rainout shelters on July 19; they remained covered until August 29 and only received 38 mm irrigation in mid Aug. The well-watered plots continued to receive rain and supplemental irrigation as needed. During this time, Canopy Temperature Depression (CT) and Normalized Differential Vegetative Index (NDVI) were collected from the ground on all plots at weekly intervals. After the shelters were removed, these measurements were collected daily for approximately 2 weeks. At the same time, Red-Green-Blue (RGB), near-infrared (NIR), and infrared (IR) images taken from an UAV platform were also collected. Vegetation indices derived from the ground and aerial data were compared with leaf wilting, pod yield and crop value. Wilting, which is a common water stress symptom, was best estimated by NDVI and RGB, and least by CT; but CT was best in estimating yield, SMK and crop value in particular when taken on the ground at 15 days water stress imposition. Interestingly, CT predicted well plant wilting even before it occurred, i.e., correlation coefficients were negative and over 0.750 when CT was measured on July 19 and 20 even though wilting was visible only after two weeks. The data, yet preliminary, show promising potential for remote sensing technologies, at the ground and aerial, for peanut variety selection for improved drought tolerance.
机译：在零星降雨的情况下，农民可以从耐旱的花生（Arachis hypogaea L.）品种中受益，产量提高。在弗吉尼亚-卡罗来纳州（VC）地区，夏季炎热加剧了干旱，通常在7月和8月发生，豆荚和种子生长旺盛。在这些生长阶段，需要每周供应50至75毫米的水以确保获利。灌溉可以补充作物的水需求，但是只有10％的花生农场得到灌溉。在这种框架下，耐旱品种可能是有利可图的，但是为提高耐旱性的品种进行育种需要快速而准确的表型。我们的目标是评估无人机遥感技术在花生抗旱选择中的潜力。在这项研究中，我们研究了干旱对23种花生品种（弗吉尼亚，亚军和巴伦西亚类型）的叶片萎wil，荚果产量，分级特性和作物价值的影响。这些变种按析因设计进行排列，其中四份重复干旱，两份重复浇水。通过在7月19日用防雨棚遮盖干旱的土地来施加干旱。他们一直被覆盖到8月29日，直到8月中旬才接受38毫米的灌溉。灌溉良好的地块继续根据需要接受降雨和补充灌溉。在这段时间里，每周从所有地块的地面上收集冠层温度降低（CT）和归一化差异植被指数（NDVI）。移走避难所后，每天收集这些测量值约2周。同时，还收集了从无人机平台拍摄的红绿蓝（RGB），近红外（NIR）和红外（IR）图像。将来自地面和空中数据的植被指数与枯萎，豆荚产量和作物价值进行了比较。枯萎是常见的水分胁迫症状，最好用NDVI和RGB估计，而用CT估计最少；但是CT可以最好地估计产量，SMK和农作物价值，尤其是在施加水分胁迫15天时在地面上进行时。有趣的是，CT甚至在植物萎发生之前就预言它会萎,,即即使在两周后才出现萎ting，相关系数也为负值，并且在7月19日和20日进行CT测量时相关系数超过0.750。数据尚未初步表明，地面和空中遥感技术有望用于选择花生品种以提高抗旱性。

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