The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of <i>Agaricus bisporus</i>, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and...
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MDPI AG
2025-01-01
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| author | Hao Ma Yulong Ding Hongwei Cui Jiangtao Ji Xin Jin Tianhang Ding Jiaoling Wang |
| author_facet | Hao Ma Yulong Ding Hongwei Cui Jiangtao Ji Xin Jin Tianhang Ding Jiaoling Wang |
| author_sort | Hao Ma |
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| description | To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of <i>Agaricus bisporus</i>, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate <i>Agaricus bisporus</i>. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest <i>Agaricus bisporus</i> based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the <i>Agaricus bisporus</i> recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of <i>Agaricus bisporus</i> and provide insight into the development of intelligent harvesting robots in the industrial production of <i>Agaricus bisporus</i>. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-d87ef11d42c94992abc5d30602198b3d2025-01-24T13:49:13ZengMDPI AGSensors1424-82202025-01-0125251910.3390/s25020519The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5sHao Ma0Yulong Ding1Hongwei Cui2Jiangtao Ji3Xin Jin4Tianhang Ding5Jiaoling Wang6College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaKey Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural Mechanization, Nanjing 210014, ChinaTo address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of <i>Agaricus bisporus</i>, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate <i>Agaricus bisporus</i>. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest <i>Agaricus bisporus</i> based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the <i>Agaricus bisporus</i> recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of <i>Agaricus bisporus</i> and provide insight into the development of intelligent harvesting robots in the industrial production of <i>Agaricus bisporus</i>.https://www.mdpi.com/1424-8220/25/2/519<i>Agaricus bisporus</i>harvesting devicemotor control algorithmmachine visionlow damage |
| spellingShingle | Hao Ma Yulong Ding Hongwei Cui Jiangtao Ji Xin Jin Tianhang Ding Jiaoling Wang The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s Sensors <i>Agaricus bisporus</i> harvesting device motor control algorithm machine vision low damage |
| title | The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s |
| title_full | The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s |
| title_fullStr | The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s |
| title_full_unstemmed | The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s |
| title_short | The Application of an Intelligent <i>Agaricus bisporus</i>-Harvesting Device Based on FES-YOLOv5s |
| title_sort | application of an intelligent i agaricus bisporus i harvesting device based on fes yolov5s |
| topic | <i>Agaricus bisporus</i> harvesting device motor control algorithm machine vision low damage |
| url | https://www.mdpi.com/1424-8220/25/2/519 |
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