A smart fruit size measuring method and system in natural environment

Abstract

The measurement of fruit size (length, width, and area) is one of the key components in the assessment of fruit ripeness for consumption and fruit quality. Due to issues with fruit shape and ambient occlusion, noncontact measurement of fruits in their natural growth state in natural environment is more difficult. This study proposes a fast and low-cost noncontact measurement solution combining deep learning, image analysis and robotic platform. First, based on the YOLOv5 object detection algorithm and converged dataset, a 95.6% detection accuracy and 0.05 s detection speed was achieved. Second, a Cycle GAN model for fruit occlusion recovery was established, and the occlusion of the fruit under different natural conditions was adaptively repaired, with a 5.48% average relative error under different occlusion conditions. Third, through image morphological operations, the size measurements were achieved, with an average relative error of 8.43% for individual fruits and the 10.12% overall error under the complex natural environment. This method and platform provide a systematic, fast (0.2s) and low-cost solution for the noncontact measurement of fruit size.