Apple crop-load estimation with over-the-row machine vision system

Abstract

Accurate crop-load estimation is important for efficient management of pre- and post-harvest operations. This information is crucial for the planning of labor and equipment requirement for harvesting and transporting fruit from the orchard to packing house. Current machine vision-based techniques for crop-load estimation have achieved only limited success mostly due to: (i) occlusion of apples by branches, leaves and/or other apples, and (ii) variable outdoor lighting conditions. In order to minimize the effect of these factors, a new sensor system was developed with an over-the-row platform integrated with a tunnel structure which acquired images from opposite sides of apple trees. The tunnel structure minimized illumination of apples with direct sunlight and reduced the variability in lighting condition. Images captured in a tall spindle orchard were processed for identifying apples, which achieved an identification accuracy of 79.8%. The location of apples in three-dimensional (3D) space was used to eliminate duplicate counting of apples that were visible to cameras from both sides of the tree canopy. The error on identifying duplicate apples was found to be 21.1%. Overall, the method achieved an accuracy of 82% on estimating crop-load on trees with dual side imaging compared to 58% with single side imaging. Over-the-row machine vision system showed promise for accurate and reliable apple crop-load estimation in the apple orchards.