Design and Experiment of a <i>Situ</i> Compensation System for Miss-Seeding of Spoon-Chain Potato Seeders

Highlights A situ compensation system for miss-seeding of spoon-chain potato seeders was designed. Human/computer interaction system for visual quality monitoring of spoon chain potato planting. The miss-seeding problem of spoon-chain potato seeder was improved. Design of miss-seeding compensation device for spoon chain potato seeder. Abstract. To address the problem of miss-seeding in spoon chain potato seeders, a set of in situ compensation systems for miss-seeding of spoon chain potato based on compensation technology was designed. The system consists of three parts: a miss-seeding detection system, a miss-seeding compensation system, and a visual monitoring system that can realize real-time feedback and reseeding of miss-seeding. The actual seeding situation of the compensated potato will be visually monitored by the industrial camera, and the seeding count will be conducted. The real-time seeding screen is displayed on the upper computer. The design of miss-seeding detection device, in situ reseeding device, structure design of miss-seeding in situ compensation system based on PLC visual monitoring human-computer interaction system were carried out. Then the potato miss-seeding compensation bench test and field test were carried out. Field experiments showed that when the seeding rate of the potato seeder was 0.3 to 0.7 m/s, the detection accuracy of the miss-seeding detection system could reach more than 98.5%, and the miss-seeding rates after reseeding were all below 5%. The visual monitoring effect of seeding quality was good, and the counting accuracy was 99.5%. The designed in situ compensation system has stable working state, light structure, high compensation and monitoring accuracy, and can solve the problem of miss-seeding in the operation of spoon and chain potato seeders. Keywords: Cut potato, In situ reseeding, Miss-seeding detection, Spoon-chain type.</jats:p

DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s

The quality inspection of potato seed tubers is pivotal for their effective segregation and a critical step in the cultivation process of potatoes. Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. This study initiates with data augmentation of the seed tuber images obtained via the image acquisition system, employing strategies such as translation, noise injection, luminance modulation, cropping, mirroring, and the Cutout technique to amplify the dataset and fortify the model&rsquo;s resilience. Subsequently, the original YOLOv5s model undergoes a series of enhancements, including the substitution of the conventional convolutional modules in the backbone network with the depth-wise separable convolution DP_Conv module to curtail the model&rsquo;s parameter count and computational load; the replacement of the original C3 module&rsquo;s Bottleneck with the GhostBottleneck to render the model more compact; and the integration of the SimAM attention mechanism module to augment the model&rsquo;s proficiency in capturing features of potato tuber buds and defects, culminating in the DCS-YOLOv5s lightweight model. The research findings indicate that the DCS-YOLOv5s model outperforms the YOLOv5s model in detection precision and velocity, exhibiting superior detection efficacy and model compactness. The model&rsquo;s detection metrics, including Precision, Recall, and mean Average Precision at Intersection over Union thresholds of 0.5 (mAP1) and 0.75 (mAP2), have improved to 95.8%, 93.2%, 97.1%, and 66.2%, respectively, signifying increments of 4.2%, 5.7%, 5.4%, and 9.8%. The detection velocity has also been augmented by 12.07%, achieving a rate of 65 FPS. The DCS-YOLOv5s target detection model, by attaining model compactness, has substantially heightened the detection precision, presenting a beneficial reference for dynamic sample target detection in the context of potato-cutting machinery

Online Recognition of Small Vegetable Seed Sowing Based on Machine Vision

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 (1.50 mm&#x2013;2.00 mm), tomato seeds (1.00 mm&#x2013;1.50 mm), and radish seeds (0.50 mm&#x2013;1.00 mm) 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&#x2019;s seeding rate monitoring is below 3.0&#x0025;. Moreover, the accuracy of missed seeding monitoring is 92.5&#x0025;, while the accuracy of deviation monitoring during seeding is 92.0&#x0025;. 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&#x0025;, 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