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Sensors 2016, 16(12), 2009; doi:10.3390/s16122009

MECS-VINE®: A New Proximal Sensor for Segmented Mapping of Vigor and Yield Parameters on Vineyard Rows

1
Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, Piacenza 29122, Italy
2
Studio di Ingegneria Terradat, via Andrea Costa 17-Paderno, Dugnano 20037 (MI), Italy
3
Appleby Italiana s.r.l., via Emilia, Roveleto di Cadeo (PC) 246/A2-29010, Italy
4
Casella Macchine Agricole s.r.l., Loc. Cimafava-Carpaneto, Piacentino 29153, Italy
*
Author to whom correspondence should be addressed.
Received: 24 September 2016 / Revised: 23 November 2016 / Accepted: 24 November 2016 / Published: 27 November 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [4433 KB, uploaded 30 November 2016]   |  

Abstract

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. View Full-Text
Keywords: precision viticulture; canopy density; Vitis vinifera L.; vine vigor; yield components precision viticulture; canopy density; Vitis vinifera L.; vine vigor; yield components
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gatti, M.; Dosso, P.; Maurino, M.; Merli, M.C.; Bernizzoni, F.; José Pirez, F.; Platè, B.; Bertuzzi, G.C.; Poni, S. MECS-VINE®: A New Proximal Sensor for Segmented Mapping of Vigor and Yield Parameters on Vineyard Rows. Sensors 2016, 16, 2009.

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