Обзор автоматических систем опрыскивания с переменной скоростью, основанной на анализе характеристик растительного покрова фруктового сада
Ключевые слова:
система опрыскивания с переменной скоростью, обнаружение и характеристика растительного покрова, глубокое обучение, машинное обучение, структурные характеристики растительного покрова, зондированиеАннотация
Использование пестицидов и загрязнение окружающей среды в садах можно значительно снизить, сочетая опрыскивание с переменной скоростью с пропорциональными системами управления. В настоящее время фермеры могут использовать опрыскивание с переменной скоростью для применения средств от сорняков только там, где они необходимы, что обеспечивает экологически чистые и экономичные химические средства для защиты растений. Кроме того, серьезной проблемой является ограничение использования пестицидов в качестве средств защиты растений (СЗР) при сохранении надлежащего отложения растительного покрова. Кроме того, автоматические опрыскиватели, которые регулируют норму внесения в соответствии с размером и формой садовых насаждений, показали значительный потенциал для сокращения использования пестицидов. Для автоматического распыления в существующем исследовании использовались искусственная нейронная сеть (ИНС) и машинное обучение. Кроме того, эффективность опрыскивания можно повысить за счет снижения потерь при распылении из-за осаждения на грунт и нецелевого сноса. Таким образом, это исследование включает в себя тщательное изучение существующих методов опрыскивания с переменной скоростью в садах. Помимо предоставления примеров их прогнозов и краткого рассмотрения влияния на параметры опрыскивания, в нем также представлены различные альтернативы предотвращению чрезмерного использования пестицидов и исследуются их преимущества и недостатки.
Литература
1. Schumann A.W., Zaman Q.U. Software development for real-time ultrasonic mapping of tree canopy size. Computers and electronics in agriculture. 2005. vol. 47. no. 1. pp. 25-40.
2. Solanelles F., Escolà A., Planas S., Rosell J.R., Camp F., Gràcia F. An electronic control system for pesticide application proportional to the canopy width of tree crops. Biosystems engineering. 2006. vol. 95. no. 4. pp. 473-481.
3. Wang H., Li S., Guo J., Liang Z. Retrieval of the leaf area density of Magnolia woody canopy with terrestrial Laser-scanning data. J. Remote Sens. 2016. vol. 20. no. 4. pp. 570-578.
4. Tumbo S.D., Salyani M., Whitney J.D., Wheaton T.A., Miller W.M. Investigation of laser and ultrasonic ranging sensors for measurements of citrus canopy volume. Applied Engineering in Agriculture. 2002. vol. 18. no. 3. p. 367.
5. Colaco A.F., Trevisan R.G., Molin J.P., Rosell-Polo J.R., Escolà A. A method to obtain orange crop geometry information using a mobile terrestrial laser scanner and 3D modeling. Remote Sensing. 2017. vol. 9. no. 8. p. 763.
6. Martínez-Casasnovas J.A., Rufat J., Arnó J., Arbonés A., Sebé F., Pascual M., Rosell-Polo J.R. Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture. 2017. vol. 18. no. 1. pp. 111-132.
7. Hu M., Whitty M. An evaluation of an apple canopy density mapping system for a variable-rate sprayer. IFAC-PapersOnLine. 2019. vol. 52. no. 30. pp. 342-348.
8. Gil E., Escolà A., Rosell J.R., Planas S., Val L. Variable rate application of plant protection products in vineyard using ultrasonic sensors. Crop Protection. 2007. vol. 26. no. 8. pp. 1287-1297.
9. Salyani M. Optimization of deposition efficiency for airblast sprayers. Transactions of the ASAE. 2000. vol. 43. no. 2. p. 247.
10. Liu H., Zhu H. Evaluation of a laser scanning sensor in detection of complex-shaped targets for variable-rate sprayer development. Transactions of the ASABE. 2016. vol. 59. no. 5. pp. 1181-1192.
11. Esau T.J., Zaman Q.U., Chang Y.K., Schumann A.W., Percival D.C., Farooque A.A. Spot-application of fungicide for wild blueberry using an automated prototype variable rate sprayer. Precision agriculture. 2014. vol. 15. no. 2. pp. 147-161.
12. Wandkar S.V., Bhatt Y.C., Jain H.K., Nalawade S.M., Pawar S.G. Real-time variable rate spraying in orchards and vineyards: A review. Journal of The Institution of Engineers (India): Series A. 2018. vol. 99. no. 2. pp. 385-390.
13. Zhang R., Song L. Study of variable spray control system based on machine vision. In 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, 2014. pp. 455-458.
14. Chen Y., Zhu H., Ozkan H.E. Development of a variable-rate sprayer with laser scanning sensor to synchronize spray outputs to tree structures. Transactions of the ASABE. 2012. vol. 55. no. 3. pp .773-781.
15. Jeon H.Y., Zhu H. Development of a variable-rate sprayer for nursery liner applications. Transactions of the ASABE. 2012. vol. 55. no. 1. pp. 303-312.
16. Liu H., Zhu H., Shen Y., Chen Y., Ozkan H.E. Development of digital flow control system for multi-channel variable-rate sprayers. Transactions of the ASABE. 2014. vol. 57. no. 1. pp. 273-281.
17. Llorens J., Gil E., Llop J., Escolà A. Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: Advances to improve pesticide application methods. Sensors. 2011. vol. 11. no. 2. pp. 2177-2194.
18. Zhu H., Ozkan E. An update on the intelligent spraying system development for fruit and nursery crop applications. In 15th Workshop on Spray Application and Precision Technology in Fruit Growing Programme and Abstracts, 2019. p.35.
19. Nackley L.L., Warneke B., Fessler L., Pscheidt J.W., Lockwood D., Wright W.C., Fulcher A. Variable-rate spray technology optimizes pesticide application by adjusting for seasonal shifts in deciduous perennial crops. HortTechnology. 2021. vol. 31. no. 4. pp. 479-489.
20. Dou H., Zhai C., Chen L., Wang X., Zou W. Comparison of Orchard Target-Oriented Spraying Systems Using Photoelectric or Ultrasonic Sensors. Agriculture. 2021. vol. 11. no. 8. pp. 753.
21. Lian Q., Tan F., Fu X., Zhang P., Liu X., Zhang W. Design of precision variable-rate spray system for unmanned aerial vehicle using automatic control method. International Journal of Agricultural and Biological Engineering. 2019. vol. 12. no. 2. pp. 29-35.
22. Kotkar V.A. An automatic pesticide sprayer to detect the crop disease using machine learning algorithms and spraying pesticide on affected crops. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021. vol. 12. no. 1S. pp. 65-72.
23. Manandhar A., Zhu H., Ozkan E., Shah A. Techno-economic impacts of using a laser-guided variable-rate spraying system to retrofit conventional constant-rate sprayers. Precision Agriculture. 2020. vol. 21. no. 5. pp. 1156-1171.
24. Shirzadifar A.M. Automatic weed detection system and smart herbicide sprayer robot for corn fields. In 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), 2013. pp. 468-473.
25. Wei Z., Xiu W., Wei D., Shuai S., Songlin W., Pengfei F. Design and test of automatic toward-target sprayer used in orchard. In 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015. pp. 697-702.
26. Berenstein R., Edan Y. Automatic adjustable spraying device for site-specific agricultural application. IEEE Transactions on Automation Science and Engineering, 2017. vol. 15. no. 2. pp. 641-650.
27. Cai J., Wang X., Gao Y., Yang S., Zhao C. Design and performance evaluation of a variable-rate orchard sprayer based on a laser-scanning sensor. International Journal of Agricultural and Biological Engineering. 2019. vol. 12. no. 6. pp. 51-57.
28. Seol J., Kim J., Son H.I. Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agriculture. 2022. vol. 23. no. 2. pp. 712-732.
29. Khodabakhshian R., Javadpour S.M. Design and development of a sensor-based precision crop protection autonomous system for orchard sprayer. Agricultural Engineering International: CIGR Journal. 2021. vol. 23. no. 3.
30. Ni M., Wang H., Liu X., Liao Y., Fu L., Wu Q., Li J. Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis. Sensors. 2021. vol. 21. no. 2. pp. 638.
31. Wen S., Zhang Q., Deng J., Lan Y., Yin X., Shan J. Design and experiment of a variable spray system for unmanned aerial vehicles based on PID and PWM control. Applied Sciences. 2018. vol. 8. no. 12. pp. 2482.
32. Maghsoudi H., Minaei S., Ghobadian B., Masoudi H. Ultrasonic sensing of pistachio canopy for low-volume precision spraying. Computers and Electronics in Agriculture. 2015. vol. 112. pp. 149-160.
33. Tewari V.K., Chandel A.K., Nare B., Kumar S. Sonar sensing predicated automatic spraying technology for orchards. Current Science. 2018. vol. 115. no. 6. pp. 1115-1123.
34. Zhou H., Jia W., Li Y., Ou M. Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology. Agriculture. 2021. vol. 11. no. 10. pp. 1011.
35. Dou H., Wang S., Zhai C., Chen L., Wang X., Zhao X. A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation. Sensors. 2021. vol. 21. no. 6. pp. 2107.
36. Mahmud M.S., Zahid A., He L., Choi D., Krawczyk G., Zhu H., Heinemann P. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications. Computers and Electronics in Agriculture. 2021. vol. 182. pp. 106053.
37. Meng Y., Zhong W., Liu Y., Wang M., Lan Y. Droplet Distribution of an Autonomous UAV-based Sprayer in Citrus Tree Canopy. In Journal of Physics: Conference Series. 2022. vol. 2203. no. 1. pp. 012022.
38. Wen S., Zhang Q., Yin X., Lan Y., Zhang J., Ge Y. Design of plant protection UAV variable spray system based on neural networks. Sensors. 2019. vol. 19. no. 5. pp. 1112.
39. Partel V., Costa L., Ampatzidis Y. Smart tree crop sprayer utilizing sensor fusion and artificial intelligence. Computers and Electronics in Agriculture. 2021. vol. 191. pp. 106556.
40. Partel V., Nunes L., Stansly P., Ampatzidis Y. Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence. Computers and Electronics in Agriculture. 2019. vol. 162. pp. 328-336.
41. Du Y., Zhang G., Tsang D., Jawed M.K. Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming. arXiv preprint arXiv:2112.13986, 2021.
42. Qin Z., Wang W., Dammer K.H., Guo L., Cao Z. A real-time low-cost artificial intelligence system for autonomous spraying in palm plantations. arXiv preprint arXiv:2103.04132, 2021.
43. Seol J., Kim J., Son H.I. Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agriculture. 2022. vol. 23. no. 2. pp. 712-732.
44. Liu J., Abbas I., Noor R.S. Development of Deep Learning-Based Variable Rate Agrochemical Spraying System for Targeted Weeds Control in Strawberry Crop. Agronomy. 2021. vol. 11. no. 8. pp. 1480.
45. Gao P., Zhang Y., Zhang L., Noguchi R., Ahamed T. Development of a recognition system for spraying areas from unmanned aerial vehicles using a machine learning approach. Sensors. 2019. vol. 19. no. 2. pp. 313.
46. Alam M., Alam M.S., Roman M., Tufail M., Khan M.U., Khan M.T. Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. In 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE), 2020. pp. 273-280.
47. Chen T., Meng F. Development and performance test of a height-adaptive pesticide spraying system. IEEE Access, 2018. vol. 6. pp. 12342-12350.
2. Solanelles F., Escolà A., Planas S., Rosell J.R., Camp F., Gràcia F. An electronic control system for pesticide application proportional to the canopy width of tree crops. Biosystems engineering. 2006. vol. 95. no. 4. pp. 473-481.
3. Wang H., Li S., Guo J., Liang Z. Retrieval of the leaf area density of Magnolia woody canopy with terrestrial Laser-scanning data. J. Remote Sens. 2016. vol. 20. no. 4. pp. 570-578.
4. Tumbo S.D., Salyani M., Whitney J.D., Wheaton T.A., Miller W.M. Investigation of laser and ultrasonic ranging sensors for measurements of citrus canopy volume. Applied Engineering in Agriculture. 2002. vol. 18. no. 3. p. 367.
5. Colaco A.F., Trevisan R.G., Molin J.P., Rosell-Polo J.R., Escolà A. A method to obtain orange crop geometry information using a mobile terrestrial laser scanner and 3D modeling. Remote Sensing. 2017. vol. 9. no. 8. p. 763.
6. Martínez-Casasnovas J.A., Rufat J., Arnó J., Arbonés A., Sebé F., Pascual M., Rosell-Polo J.R. Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture. 2017. vol. 18. no. 1. pp. 111-132.
7. Hu M., Whitty M. An evaluation of an apple canopy density mapping system for a variable-rate sprayer. IFAC-PapersOnLine. 2019. vol. 52. no. 30. pp. 342-348.
8. Gil E., Escolà A., Rosell J.R., Planas S., Val L. Variable rate application of plant protection products in vineyard using ultrasonic sensors. Crop Protection. 2007. vol. 26. no. 8. pp. 1287-1297.
9. Salyani M. Optimization of deposition efficiency for airblast sprayers. Transactions of the ASAE. 2000. vol. 43. no. 2. p. 247.
10. Liu H., Zhu H. Evaluation of a laser scanning sensor in detection of complex-shaped targets for variable-rate sprayer development. Transactions of the ASABE. 2016. vol. 59. no. 5. pp. 1181-1192.
11. Esau T.J., Zaman Q.U., Chang Y.K., Schumann A.W., Percival D.C., Farooque A.A. Spot-application of fungicide for wild blueberry using an automated prototype variable rate sprayer. Precision agriculture. 2014. vol. 15. no. 2. pp. 147-161.
12. Wandkar S.V., Bhatt Y.C., Jain H.K., Nalawade S.M., Pawar S.G. Real-time variable rate spraying in orchards and vineyards: A review. Journal of The Institution of Engineers (India): Series A. 2018. vol. 99. no. 2. pp. 385-390.
13. Zhang R., Song L. Study of variable spray control system based on machine vision. In 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, 2014. pp. 455-458.
14. Chen Y., Zhu H., Ozkan H.E. Development of a variable-rate sprayer with laser scanning sensor to synchronize spray outputs to tree structures. Transactions of the ASABE. 2012. vol. 55. no. 3. pp .773-781.
15. Jeon H.Y., Zhu H. Development of a variable-rate sprayer for nursery liner applications. Transactions of the ASABE. 2012. vol. 55. no. 1. pp. 303-312.
16. Liu H., Zhu H., Shen Y., Chen Y., Ozkan H.E. Development of digital flow control system for multi-channel variable-rate sprayers. Transactions of the ASABE. 2014. vol. 57. no. 1. pp. 273-281.
17. Llorens J., Gil E., Llop J., Escolà A. Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: Advances to improve pesticide application methods. Sensors. 2011. vol. 11. no. 2. pp. 2177-2194.
18. Zhu H., Ozkan E. An update on the intelligent spraying system development for fruit and nursery crop applications. In 15th Workshop on Spray Application and Precision Technology in Fruit Growing Programme and Abstracts, 2019. p.35.
19. Nackley L.L., Warneke B., Fessler L., Pscheidt J.W., Lockwood D., Wright W.C., Fulcher A. Variable-rate spray technology optimizes pesticide application by adjusting for seasonal shifts in deciduous perennial crops. HortTechnology. 2021. vol. 31. no. 4. pp. 479-489.
20. Dou H., Zhai C., Chen L., Wang X., Zou W. Comparison of Orchard Target-Oriented Spraying Systems Using Photoelectric or Ultrasonic Sensors. Agriculture. 2021. vol. 11. no. 8. pp. 753.
21. Lian Q., Tan F., Fu X., Zhang P., Liu X., Zhang W. Design of precision variable-rate spray system for unmanned aerial vehicle using automatic control method. International Journal of Agricultural and Biological Engineering. 2019. vol. 12. no. 2. pp. 29-35.
22. Kotkar V.A. An automatic pesticide sprayer to detect the crop disease using machine learning algorithms and spraying pesticide on affected crops. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021. vol. 12. no. 1S. pp. 65-72.
23. Manandhar A., Zhu H., Ozkan E., Shah A. Techno-economic impacts of using a laser-guided variable-rate spraying system to retrofit conventional constant-rate sprayers. Precision Agriculture. 2020. vol. 21. no. 5. pp. 1156-1171.
24. Shirzadifar A.M. Automatic weed detection system and smart herbicide sprayer robot for corn fields. In 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), 2013. pp. 468-473.
25. Wei Z., Xiu W., Wei D., Shuai S., Songlin W., Pengfei F. Design and test of automatic toward-target sprayer used in orchard. In 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015. pp. 697-702.
26. Berenstein R., Edan Y. Automatic adjustable spraying device for site-specific agricultural application. IEEE Transactions on Automation Science and Engineering, 2017. vol. 15. no. 2. pp. 641-650.
27. Cai J., Wang X., Gao Y., Yang S., Zhao C. Design and performance evaluation of a variable-rate orchard sprayer based on a laser-scanning sensor. International Journal of Agricultural and Biological Engineering. 2019. vol. 12. no. 6. pp. 51-57.
28. Seol J., Kim J., Son H.I. Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agriculture. 2022. vol. 23. no. 2. pp. 712-732.
29. Khodabakhshian R., Javadpour S.M. Design and development of a sensor-based precision crop protection autonomous system for orchard sprayer. Agricultural Engineering International: CIGR Journal. 2021. vol. 23. no. 3.
30. Ni M., Wang H., Liu X., Liao Y., Fu L., Wu Q., Li J. Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis. Sensors. 2021. vol. 21. no. 2. pp. 638.
31. Wen S., Zhang Q., Deng J., Lan Y., Yin X., Shan J. Design and experiment of a variable spray system for unmanned aerial vehicles based on PID and PWM control. Applied Sciences. 2018. vol. 8. no. 12. pp. 2482.
32. Maghsoudi H., Minaei S., Ghobadian B., Masoudi H. Ultrasonic sensing of pistachio canopy for low-volume precision spraying. Computers and Electronics in Agriculture. 2015. vol. 112. pp. 149-160.
33. Tewari V.K., Chandel A.K., Nare B., Kumar S. Sonar sensing predicated automatic spraying technology for orchards. Current Science. 2018. vol. 115. no. 6. pp. 1115-1123.
34. Zhou H., Jia W., Li Y., Ou M. Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology. Agriculture. 2021. vol. 11. no. 10. pp. 1011.
35. Dou H., Wang S., Zhai C., Chen L., Wang X., Zhao X. A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation. Sensors. 2021. vol. 21. no. 6. pp. 2107.
36. Mahmud M.S., Zahid A., He L., Choi D., Krawczyk G., Zhu H., Heinemann P. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications. Computers and Electronics in Agriculture. 2021. vol. 182. pp. 106053.
37. Meng Y., Zhong W., Liu Y., Wang M., Lan Y. Droplet Distribution of an Autonomous UAV-based Sprayer in Citrus Tree Canopy. In Journal of Physics: Conference Series. 2022. vol. 2203. no. 1. pp. 012022.
38. Wen S., Zhang Q., Yin X., Lan Y., Zhang J., Ge Y. Design of plant protection UAV variable spray system based on neural networks. Sensors. 2019. vol. 19. no. 5. pp. 1112.
39. Partel V., Costa L., Ampatzidis Y. Smart tree crop sprayer utilizing sensor fusion and artificial intelligence. Computers and Electronics in Agriculture. 2021. vol. 191. pp. 106556.
40. Partel V., Nunes L., Stansly P., Ampatzidis Y. Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence. Computers and Electronics in Agriculture. 2019. vol. 162. pp. 328-336.
41. Du Y., Zhang G., Tsang D., Jawed M.K. Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming. arXiv preprint arXiv:2112.13986, 2021.
42. Qin Z., Wang W., Dammer K.H., Guo L., Cao Z. A real-time low-cost artificial intelligence system for autonomous spraying in palm plantations. arXiv preprint arXiv:2103.04132, 2021.
43. Seol J., Kim J., Son H.I. Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agriculture. 2022. vol. 23. no. 2. pp. 712-732.
44. Liu J., Abbas I., Noor R.S. Development of Deep Learning-Based Variable Rate Agrochemical Spraying System for Targeted Weeds Control in Strawberry Crop. Agronomy. 2021. vol. 11. no. 8. pp. 1480.
45. Gao P., Zhang Y., Zhang L., Noguchi R., Ahamed T. Development of a recognition system for spraying areas from unmanned aerial vehicles using a machine learning approach. Sensors. 2019. vol. 19. no. 2. pp. 313.
46. Alam M., Alam M.S., Roman M., Tufail M., Khan M.U., Khan M.T. Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. In 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE), 2020. pp. 273-280.
47. Chen T., Meng F. Development and performance test of a height-adaptive pesticide spraying system. IEEE Access, 2018. vol. 6. pp. 12342-12350.
Опубликован
2023-01-27
Как цитировать
Патил, С. С., Патил, Ю. М., & Патил, С. Б. (2023). Обзор автоматических систем опрыскивания с переменной скоростью, основанной на анализе характеристик растительного покрова фруктового сада. Информатика и автоматизация, 22(1), 57-86. https://doi.org/10.15622/ia.22.1.3
Раздел
Робототехника, автоматизация и системы управления
Copyright (c) Seema S. Patil
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Авторы, которые публикуются в данном журнале, соглашаются со следующими условиями: Авторы сохраняют за собой авторские права на работу и передают журналу право первой публикации вместе с работой, одновременно лицензируя ее на условиях Creative Commons Attribution License, которая позволяет другим распространять данную работу с обязательным указанием авторства данной работы и ссылкой на оригинальную публикацию в этом журнале. Авторы сохраняют право заключать отдельные, дополнительные контрактные соглашения на неэксклюзивное распространение версии работы, опубликованной этим журналом (например, разместить ее в университетском хранилище или опубликовать ее в книге), со ссылкой на оригинальную публикацию в этом журнале. Авторам разрешается размещать их работу в сети Интернет (например, в университетском хранилище или на их персональном веб-сайте) до и во время процесса рассмотрения ее данным журналом, так как это может привести к продуктивному обсуждению, а также к большему количеству ссылок на данную опубликованную работу (Смотри The Effect of Open Access).