Assessing a multi-camera system to enhance fruit visibility for robotic harvesting in a V-trellised apple orchard

Abstract

Accurate detection and localization of fruits within the canopy are crucial for various tasks, such as perception for robotic harvesters, vision-based yield estimation, and early disease detection. However, complex canopy structures render fruit detection challenging due to obstructions from leaves, branches, and other fruits. This study assesses the efficacy of utilizing multiple cameras at varying elevations and azimuthal angles to enhance fruit visibility. We used a V-trellised apple orchard as a case study, where four cameras were employed; the first was oriented normally to the fruit canopy, while three others maintained a fixed position relative to the first but with variable orientation. Adjusting elevation and azimuthal angles revealed that, in the region of interest of the canopy, a single camera whose optical axis was perpendicular to the canopy could detect up to 88.3% of the fruits detected by four cameras. Adding one more camera – for a total of two cameras – increased the detection rate up to 97.5% of the four-camera detection rate. Also, the experimental results showed that three cameras could detect between 96.0% to 99.7% of fruits compared to four cameras under the proposed camera configuration. Hence, the utility of adding a third or fourth camera would need to be considered carefully, given the added cost and complexity. The results of this study are a step toward exploring and optimizing multiple-camera perception systems for orchard operations, in particular, robotic harvesters.