Online Recognition of Small Vegetable Seed Sowing Based on Machine Vision

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Zhang, Weipeng, Zhao, Bo, Gao, Shengbo, Ji, Yuxi, Zhou, Liming, Niu, Kang, Qiu, Zhaomei, Jin, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; algorithm comparison; Algorithm design and analysis; Algorithms; Belts; Classification algorithms; Computer terminals; Coordinates; Critical point; Deviation; Feature extraction; Image filters; Image processing; Image recognition; Image segmentation; Machine vision; Monitoring; monitoring of sowing quality; Particle size distribution; Planting; Real-time systems; Seeds; Seeds (agriculture); Small vegetable seeds; Vegetables; Vegetation; Vision systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3336944

The lightweight, small diameter, and irregular shape of small vegetable seeds create difficulties for online monitoring of sowing quality. We propose a machine vision-based online monitoring method with a sowing test bench designed to address the challenges. Vision devices and image processing systems are employed to detect the quality of seed sowing. Firstly, the seed segmentation image is obtained by completing the steps of median filtering, graying and image segmentation.We then implement the Circumscribed circle method to detect the position of the seed. Afterward, the coordinate system is converted using calibrated results to eliminate non-seed impurities. Finally, we count the number of identified seeds to evaluate the recognition accuracy. The trial compared three algorithms: the image segmentation algorithm OTSU, the critical point localization algorithm SIFT, and the algorithm designed in the experiment. The algorithm we designed outperformed the others regarding recognition accuracy and processing time. The experimental method employed in the study encompasses various functionalities, including seeding counting, understanding detection, replaying, and monitoring deviations from seed bands during sowing. Cabbage seeds (1.50mm-2.00mm), tomato seeds (1.00mm-1.50mm), and radish seeds (0.50mm-1.00mm) were selected as the experimental subjects due to the uniform particle size distribution. The results demonstrate that the relative error between the online image recognition algorithm and the system's seeding rate monitoring is below 3.0%. Moreover, the accuracy of missed seeding monitoring is 92.5%, while the accuracy of deviation monitoring during seeding is 92.0%. We observed that the image recognition algorithm employed in the system achieved a processing time of 0.29 seconds, with a seed band recognition rate of 96.8%, fulfilling the monitoring requirements for small seed sowing experiments. The processed images and collected data are presented in real-time on the upper computer terminal. This study significantly contributes to the advancement of small-grain vegetable seed sowing monitoring technology.