Non-Productive Vine Canopy Estimation through Proximal and Remote Sensing*. Issue 16 (2016)

• Record Type: Journal Article
• Authors: Tang, Julie; Woods, Michael; Cossell, Stephen; Liu, Scarlett; Whitty, Mark
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 16(2016)
• Page Start: 398
• Page End: 403
• Publication Date: 2016
• Subjects: NDVI -- yield estimation -- decision support systems -- automation -- mapping -- machine learning; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.10.073
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Non-productive canopy detection in a viticultural block is a key factor in reducing the drain on infrastructure and improving management practices. However, current methods are significant in cost, biased, and do not provide information on location of non-productive canopy. This paper proposes both a proximal and remote sensing method for assisting in decision support and yield estimation from available technologies. The proximal method utilizes two different measures of green pixel thresholding in video frames, with results providing a useful relative measure of productivity across a vineyard block at the phenological stage of shoots. The remote sensing method utilizes local thresholding and Self-Organizing-Maps on aerial imagery to identify missing vines and total non-productive canopy on a block level. Results indicate the success of this semi-supervised method in providing a useful measure of non-productive canopy at the phenological stage of veraison; laying the groundwork for improved methods in this area. These methods provide practical outputs that lay the foundations for improving management decisions in an automatic and low-cost manner at different times in the season.