Development, Design, and Improvement of an Intelligent Harvesting System for Aquatic Vegetable <i>Brasenia schreberi</i>
At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as <i>Brasenia schreberi</i> relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the in...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/6/1451 |
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| Summary: | At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as <i>Brasenia schreberi</i> relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable <i>Brasenia schreberi</i> was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness. |
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| ISSN: | 2073-4395 |