Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design
The complexity of natural elements seriously affects the accuracy and stability of field target identification, and the speed of an identification algorithm essentially limits the practical application of field pesticide spraying. In this study, a cabbage identification and pesticide spraying contro...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.924973/full |
| _version_ | 1811313469002088448 |
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| author | Xueguan Zhao Xueguan Zhao Xiu Wang Xiu Wang Cuiling Li Cuiling Li Hao Fu Hao Fu Shuo Yang Shuo Yang Changyuan Zhai Changyuan Zhai |
| author_facet | Xueguan Zhao Xueguan Zhao Xiu Wang Xiu Wang Cuiling Li Cuiling Li Hao Fu Hao Fu Shuo Yang Shuo Yang Changyuan Zhai Changyuan Zhai |
| author_sort | Xueguan Zhao |
| collection | DOAJ |
| description | The complexity of natural elements seriously affects the accuracy and stability of field target identification, and the speed of an identification algorithm essentially limits the practical application of field pesticide spraying. In this study, a cabbage identification and pesticide spraying control system based on an artificial light source was developed. With the image skeleton point-to-line ratio and ring structure features of support vector machine classification and identification, a contrast test of different feature combinations of a support vector machine was carried out, and the optimal feature combination of the support vector machine and its parameters were determined. In addition, a targeted pesticide spraying control system based on an active light source and a targeted spraying delay model were designed, and a communication protocol for the targeted spraying control system based on electronic control unit was developed to realize the controlled pesticide spraying of targets. According to the results of the support vector machine classification test, the feature vector comprised of the point-to-line ratio, maximum inscribed circle radius, and fitted curve coefficient had the highest identification accuracy of 95.7%, with a processing time of 33 ms for a single-frame image. Additionally, according to the results of a practical field application test, the average identification accuracies of cabbage were 95.0%, average identification accuracies of weed were 93.5%, and the results of target spraying at three operating speeds of 0.52 m/s, 0.69 m/s and 0.93 m/s show that the average invalid spraying rate, average missed spraying rate, and average effective spraying rate were 2.4, 4.7, and 92.9%, respectively. Moreover, it was also found from the results that with increasing speeds, the offset of the centre of the mass of the target increased and reached a maximum value of 28.6 mm when the speed was 0.93 m/s. The void rate and pesticide saving rate were 65 and 33.8% under continuous planting conditions and 76.6 and 53.3% under natural seeding deficiency conditions, respectively. |
| first_indexed | 2024-04-13T10:54:13Z |
| format | Article |
| id | doaj.art-3d0dcb00593944819074a33fc82944a8 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-13T10:54:13Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-3d0dcb00593944819074a33fc82944a82022-12-22T02:49:33ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-08-011310.3389/fpls.2022.924973924973Cabbage and Weed Identification Based on Machine Learning and Target Spraying System DesignXueguan Zhao0Xueguan Zhao1Xiu Wang2Xiu Wang3Cuiling Li4Cuiling Li5Hao Fu6Hao Fu7Shuo Yang8Shuo Yang9Changyuan Zhai10Changyuan Zhai11Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaCollege of Mechanical Engineering, Guangxi University, Beijing, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing, ChinaThe complexity of natural elements seriously affects the accuracy and stability of field target identification, and the speed of an identification algorithm essentially limits the practical application of field pesticide spraying. In this study, a cabbage identification and pesticide spraying control system based on an artificial light source was developed. With the image skeleton point-to-line ratio and ring structure features of support vector machine classification and identification, a contrast test of different feature combinations of a support vector machine was carried out, and the optimal feature combination of the support vector machine and its parameters were determined. In addition, a targeted pesticide spraying control system based on an active light source and a targeted spraying delay model were designed, and a communication protocol for the targeted spraying control system based on electronic control unit was developed to realize the controlled pesticide spraying of targets. According to the results of the support vector machine classification test, the feature vector comprised of the point-to-line ratio, maximum inscribed circle radius, and fitted curve coefficient had the highest identification accuracy of 95.7%, with a processing time of 33 ms for a single-frame image. Additionally, according to the results of a practical field application test, the average identification accuracies of cabbage were 95.0%, average identification accuracies of weed were 93.5%, and the results of target spraying at three operating speeds of 0.52 m/s, 0.69 m/s and 0.93 m/s show that the average invalid spraying rate, average missed spraying rate, and average effective spraying rate were 2.4, 4.7, and 92.9%, respectively. Moreover, it was also found from the results that with increasing speeds, the offset of the centre of the mass of the target increased and reached a maximum value of 28.6 mm when the speed was 0.93 m/s. The void rate and pesticide saving rate were 65 and 33.8% under continuous planting conditions and 76.6 and 53.3% under natural seeding deficiency conditions, respectively.https://www.frontiersin.org/articles/10.3389/fpls.2022.924973/fulltarget sprayingindependent nozzle controltarget identificationeffective spraying ratepesticide saving amount |
| spellingShingle | Xueguan Zhao Xueguan Zhao Xiu Wang Xiu Wang Cuiling Li Cuiling Li Hao Fu Hao Fu Shuo Yang Shuo Yang Changyuan Zhai Changyuan Zhai Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design Frontiers in Plant Science target spraying independent nozzle control target identification effective spraying rate pesticide saving amount |
| title | Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design |
| title_full | Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design |
| title_fullStr | Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design |
| title_full_unstemmed | Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design |
| title_short | Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design |
| title_sort | cabbage and weed identification based on machine learning and target spraying system design |
| topic | target spraying independent nozzle control target identification effective spraying rate pesticide saving amount |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.924973/full |
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