Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming
Due to large line spacing and planting distances, the adoption of continuous and uniform pesticide spraying in vegetable farming can lead to pesticide waste, thus increasing cost and environmental pollution. In this paper, by applying deep learning and online identification methods, control technolo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/10/2551 |
| _version_ | 1797476079075590144 |
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| author | Hao Fu Xueguan Zhao Huarui Wu Shenyu Zheng Kang Zheng Changyuan Zhai |
| author_facet | Hao Fu Xueguan Zhao Huarui Wu Shenyu Zheng Kang Zheng Changyuan Zhai |
| author_sort | Hao Fu |
| collection | DOAJ |
| description | Due to large line spacing and planting distances, the adoption of continuous and uniform pesticide spraying in vegetable farming can lead to pesticide waste, thus increasing cost and environmental pollution. In this paper, by applying deep learning and online identification methods, control technology for target-oriented spraying is studied with cabbages as the research object. To overcome motion blur and low average precision under strong light conditions during the operation of sprayers, an innovative YOLOV5 model implanted with a transformer module is utilized to achieve accurate online identification for cabbage fields under complex environments. Based on this concept, a new target-oriented spray system is built on an NVIDIA Jetson Xavier NX. Indoor test results show that the average precision is 96.14% and the image processing time is 51.07 ms. When motion blur occurs, the average precision for the target is 90.31%. Then, in a field experiment, when the light intensity is within the range of 3.76–12.34 wlx, the advance opening distance is less than 3.51 cm, the delay closing distance is less than 2.05 cm, and the average identification error for the cabbage diameter is less than 1.45 cm. The experimental results indicate that changes in light intensity have no significant impact on the identification effect. The average precision is 98.65%, and the savings rate reaches 54.04%. In general, the target-oriented spray system designed in this study achieves the expected experimental results and can provide technical support for field target spraying. |
| first_indexed | 2024-03-09T20:52:54Z |
| format | Article |
| id | doaj.art-c04dafc28aef42378e3be216a43d0e34 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:52:54Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-c04dafc28aef42378e3be216a43d0e342023-11-23T22:29:09ZengMDPI AGAgronomy2073-43952022-10-011210255110.3390/agronomy12102551Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage FarmingHao Fu0Xueguan Zhao1Huarui Wu2Shenyu Zheng3Kang Zheng4Changyuan Zhai5Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaDue to large line spacing and planting distances, the adoption of continuous and uniform pesticide spraying in vegetable farming can lead to pesticide waste, thus increasing cost and environmental pollution. In this paper, by applying deep learning and online identification methods, control technology for target-oriented spraying is studied with cabbages as the research object. To overcome motion blur and low average precision under strong light conditions during the operation of sprayers, an innovative YOLOV5 model implanted with a transformer module is utilized to achieve accurate online identification for cabbage fields under complex environments. Based on this concept, a new target-oriented spray system is built on an NVIDIA Jetson Xavier NX. Indoor test results show that the average precision is 96.14% and the image processing time is 51.07 ms. When motion blur occurs, the average precision for the target is 90.31%. Then, in a field experiment, when the light intensity is within the range of 3.76–12.34 wlx, the advance opening distance is less than 3.51 cm, the delay closing distance is less than 2.05 cm, and the average identification error for the cabbage diameter is less than 1.45 cm. The experimental results indicate that changes in light intensity have no significant impact on the identification effect. The average precision is 98.65%, and the savings rate reaches 54.04%. In general, the target-oriented spray system designed in this study achieves the expected experimental results and can provide technical support for field target spraying.https://www.mdpi.com/2073-4395/12/10/2551precision agricultureprecision pesticide sprayingdeep learningtarget-oriented spraytarget identification |
| spellingShingle | Hao Fu Xueguan Zhao Huarui Wu Shenyu Zheng Kang Zheng Changyuan Zhai Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming Agronomy precision agriculture precision pesticide spraying deep learning target-oriented spray target identification |
| title | Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming |
| title_full | Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming |
| title_fullStr | Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming |
| title_full_unstemmed | Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming |
| title_short | Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming |
| title_sort | design and experimental verification of the yolov5 model implanted with a transformer module for target oriented spraying in cabbage farming |
| topic | precision agriculture precision pesticide spraying deep learning target-oriented spray target identification |
| url | https://www.mdpi.com/2073-4395/12/10/2551 |
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