Computer vision system for wild blueberry fruit yield mapping

Abstract

Wild blueberry (Vaccinium angustifolium Ait.) is a unique crop native to Northeastern North America with annual production of 82,000 t from an area of 79,000 ha. Yield maps along with topography and soil nutrient maps could be used to develop precise site-specific nutrition programs for wild blueberry production. Here, the development of an automated yield monitoring and mapping system for real-time fruit yield estimation for wild blueberry is presented. The hardware components of the system consist of a digital colour camera, a positioning system receiver and a computer mounted on a specially designed farm motorised vehicle. The yield prediction method was based on the estimation of the blue pixels representing ripe fruit in the field of view of each image and their expression as a percentage of total image pixels, using it as the correlation parameter with the corresponding yield. The image processing software was development in “Delphi” and “C” programming languages. Field experiments were carried out in two commercial wild blueberry fields. Concerning the calibration of the system, the experimental results show a significant correlation between percentage blue pixels and actual fruit yield (R2 = 0.90; P