Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct application on edge computing devices due to its high computational cost. To make the network more compact and to address the problems of low detection accuracy and long inference time, this study proposes an improved YOLO v3 model, in which some redundant channels and network layers are pruned to achieve real-time determination of ginger shoots and seeds. The test results showed that the pruned model reduced its model size by 87.2% and improved the detection speed by 85%. Meanwhile, its mean average precision (mAP) reached 98.0% for ginger shoots and seeds, only 0.1% lower than the model before pruning. Moreover, after deploying the model to the Jetson Nano, the test results showed that its mAP was 97.94%, the recognition accuracy could reach 96.7%, and detection speed could reach 20 frames·s−1. The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding.
Quality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a problem to be solved urgently for antler mushroom industrial development with increasing labor costs. To solve the problem, this paper deeply integrates the single-stage object detection of YOLOv5 and the semantic segmentation of PSPNet, and proposes a Y-PNet model for real-time object detection and an image segmentation network. This article also proposes an evaluation model for antler mushroom’s size, which eliminates subjective judgment and achieves quality grading. Moreover, to meet the needs of efficient and accurate hierarchical detection in the factory, this study uses the lightweight network model to construct a lightweight YOLOv5 single-stage object detection model. The MobileNetV3 network model embedded with a CBAM module is used as the backbone extractor in PSPNet to reduce the model’s size and improve the model’s efficiency and accuracy for segmentation. Experiments show that the proposed system can perform real-time grading successfully, which can provide instructive and practical references in industry.
Green onion (Allium fistulosum L.) is mainly available as factory-produced seedlings. Although factory seedling production is highly automated, miss-seeding during the seeding process considerably affects subsequent transplanting and the final yield. To solve the problem of miss-seeding, the current main method is manual complementary seeding, which is labor-intensive and inefficient work. In this study, an automatic machine-vision-based complementary seeding device was proposed to reduce the miss-seeding rate and as a replacement of manual complementary seeding. The device performs several main functions, including the identification of miss-seeding holes, control of seed case movement, and the seed uptake and release from the seed suction nozzle array. A majority-mechanism-based miss-seeding tray hole rapid-detection method was proposed to enable the real-time identification of miss-seeding tray holes in the tray under high-speed moving conditions. The structural parameters of the vacuum-generated seed suction nozzle were optimized through numerical simulations and orthogonal experiments, and the seed suction nozzle array and seed case were produced using 3D-printing technology. Finally, the complementary seeding device was installed on the tray-type green onion seeding machine and the effectiveness of the complementary seeding was confirmed by experiments. The results revealed that the average values of the precision, recall, and F1 scores for identifying miss-seeding tray holes were 98.48%, 97.00%, and 97.73%, respectively. The results revealed that the rate of miss-seeding tray holes decreased from 5.37% to 0.89% after complementary seeding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.