Automated Crop Yield Estimation for Apple Orchards

Abstract

Crop yield estimation is an important task in apple orchard management. The current manual sampling-based yield estimation is time-consuming, labor-intensive and inaccurate. To deal with this challenge, we develop and deploy a computer vision system for automated, rapid and accurate yield estimation. The system uses a two-camera stereo rig for image acquisition. It works at nighttime with controlled artificial lighting to reduce the variance of natural illumination. An autonomous orchard vehicle is used as the support platform for automated data collection. The system scans the both sides of each tree row in orchards. A computer vision algorithm is developed to detect and register apples from acquired sequential images, and then generate apple counts as crop yield estimation. We de- ployed the yield estimation system in Washington state in September, 2011. The results show that the developed system works well with both red and green apples in the tall-spindle planting system. The errors of crop yield estimation are -3.2% for a red apple block with about 480 trees, and 1.2% for a green apple block with about 670 trees.