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MECS-VINE®: A new proximal sensor for segmented mapping of vigor and yield parameters on vineyard rows

机译:MECS-VINE®:一种新的近端传感器,用于对葡萄园行上的活力和产量参数进行分段映射

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摘要

Ground-based proximal sensing of vineyard features is gaining interest due to its ability to serve in even quite small plots with the advantage of being conducted concurrently with normal vineyard practices (i.e., spraying, pruning or soil tilling) with no dependence upon weather conditions, external services or law-imposed limitations. The purpose of the present work was to test performance of the new terrestrial multi-sensor MECS-VINE® in terms of reliability and degree of correlation with several canopy growth and yield parameters in the grapevine. MECS-VINE®, once conveniently positioned in front of the tractor, can provide simultaneous assessment of growth features and microclimate of specific canopy sections of the two adjacent row sides. MECS-VINE® integrates a series of microclimate sensors (air relative humidity, air and surface temperature) with two (left and right) matrix-based optical RGB imaging sensors and a related algorithm, termed Canoyct). MECS-VINE® was run five times along the season in a mature cv. Barbera vineyard and a Canopy Index (CI, pure number varying from 0 to 1000), calculated through its built-in algorithm, validated vs. canopy structure parameters (i.e., leaf layer number, fractions of canopy gaps and interior leaves) derived from point quadrat analysis. Results showed that CI was highly correlated vs. any canopy parameter at any date, although the closest relationships were found for CI vs. fraction of canopy gaps (R2 = 0.97) and leaf layer number (R2 = 0.97) for data pooled over 24 test vines. While correlations against canopy light interception and total lateral leaf area were still unsatisfactory, a good correlation was found vs. cluster and berry weight (R2 = 0.76 and 0.71, respectively) suggesting a good potential also for yield estimates. Besides the quite satisfactory calibration provided, main improvements of MECS-VINE® usage versus other current equipment are: (i) MECS-VINE® delivers a segmented evaluation of the canopy up to 15 different sectors, therefore allowing to differentiate canopy structure and density at specific and crucial canopy segments (i.e., basal part where clusters are located) and (ii) the sensor is optimized to work at any time of the day with any weather condition without the need of any supplemental lighting system.
机译:基于地面的葡萄园特征近端传感技术越来越受到关注,因为它能够在很小的土地上使用,并且具有与常规葡萄园实践(即喷洒,修剪或土壤耕作)同时进行且不受天气条件影响的优势,外部服务或法律规定的限制。本工作的目的是测试新的陆地多传感器MECS-VINE®的性能,以及与葡萄中几个冠层生长和产量参数的相关性和可靠性。 MECS-VINE®曾经很方便地放置在拖拉机的前面,可以同时评估两个相邻行侧特定树冠部分的生长特征和微气候。 MECS-VINE®将一系列微气候传感器(空气相对湿度,空气和表面温度)与两个(左右)基于矩阵的光学RGB成像传感器和相关算法(称为Canoyct)集成在一起。 MECS-VINE®在一个成熟的简历中,整个赛季运行了五次。 Barbera葡萄园和通过其内置算法计算的冠层指数(CI,纯数字从0到1000不等),验证了从点得出的冠层结构参数与冠层结构参数(即叶层数,冠层间隙和内部叶的分数)之间的关系正交分析。结果表明,在任何日期,CI都与任何冠层参数高度相关,尽管在24个测试中收集的数据中,CI与冠层间隙分数(R2 = 0.97)和叶层数(R2 = 0.97)之间存在最密切的关系。藤蔓。尽管与冠层光截留和总侧叶面积的相关性仍不令人满意,但与簇重和浆果重量之间的相关性很好(分别为R2 = 0.76和0.71),这也暗示了单产估计的潜力。除了提供令人满意的校准外,与其他现有设备相比,MECS-VINE®的使用还有以下主要改进:(i)MECS-VINE®可以对多达15个不同扇区的冠层进行分段评估,因此可以区分冠层结构和密度。特定且关键的树冠部分(即簇所在的基础部分)和(ii)传感器经过优化,可在一天中的任何时间,任何天气条件下工作,而无需任何辅助照明系统。

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