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

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 (² = 0.97) and leaf layer number (² = 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 (² = 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.