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Tarımda Optimal Drone Uçuş Rotası Planlaması İçin Genetik ve Açgözlü Algoritmanın Karşılaştırmalı Analizi

Year 2024, Volume: 39 Issue: 1, 129 - 142, 29.02.2024

Eray Önler

https://doi.org/10.7161/omuanajas.1394616

Abstract

Bu çalışmada Genetik Algoritmanın (GA) tarımsal dronların uçuş rotasını optimize etmedeki performansı Açgözlü Algoritma ile karşılaştırılmıştır. GA'nın ortalama %17,44 daha kısa rotalar ürettiği görülmüştür. Statik olarak simüle edilen bir saha modelinde, genetik algoritmada 500 nesil üzerinden ölçülen bu verimlilik, tarımsal faaliyetlerde kaynak ve zaman tasarrufu açısından önemli bir potansiyele işaret etmektedir. GA'nın etkinliğine rağmen hesaplama yoğunluğu, gerçek zamanlı saha uygulamalarını sınırlandırmaktadır, ancak önceden uygulama haritası hazırlanmış alanlar için çevrimdışı rota planlamada avantajlar sunmaktadır. Algoritmaların rastgele olarak üretilen uçuş simülasyonlarında üretmiş oldukları rota uzunlukları arasında t-testi kullanılarak yapılan karşılaştırmada GA tarafından üretilen rotaların istatistiksel olarak anlamlı seviyede kısa olduğu görülmüştür (p=7.18×10−9). Gelecekteki araştırmalarda, GA'ların dron rota optimizasyonunda pratik kullanımını geliştirmek için simülasyonda kullanılan basitleştirilmiş model ile gerçek dünya uygulamalarındaki karmaşıklık arasında bulunan farkları gidermek amaçlanacaktır.

Keywords

Dron rota optimizasyonu tarımsal dron çevrimdışı rota planlama

References

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  • Banpurkar, R., Raut, A.K., Ramteke, P.P., Prajapati, A.S., Sevaklal, A., Gautam, G.A.D., Bambole, A.S., 2021. Fertilizer Spraying UAV-A Review on Agriculture Drone.
  • Basiri, A., Mariani, V., Silano, G., Aatif, M., Iannelli, L., Glielmo, L., 2022. A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture. The Journal of Navigation, 75(2), 364-383. doi: 10.1017/S0373463321000825.
  • Cheikhrouhou, O., Khoufi, I., 2021. A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy. Computer Science Review, 40, 100369. doi: 10.1016/j.cosrev.2021.100369.
  • Chen, C.J., Huang, Y.Y., Li, Y.S., Chen, Y.C., Chang, C.Y., Huang, Y.M., 2021. Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access, 9, 21986-21997. doi: 10.1109/ACCESS.2021.3056082.
  • Delay, N.D., Thompson, N.M., Mintert, J.R., 2022. Precision agriculture technology adoption and technical efficiency. Journal of Agricultural Economics, 73(1), 195-219. doi: 10.1111/1477-9552.12440.
  • Edwards, C.A., 2020. The importance of integration in sustainable agricultural systems. In Sustainable agricultural systems (pp. 249-264). CRC Press
  • Fortin, F.A., De Rainville, F.M., Gardner, M.A.G., Parizeau, M., Gagné, C., 2012. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1), 2171-2175.
  • Hafeez, A., Husain, M.A., Singh, S.P., Chauhan, A., Khan, M.T., Kumar, N., Chauhan, A., Soni, S.K., 2022. Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture. doi: 10.1016/j.inpa.2022.02.002.
  • Hunter, J.D., 2007. Matplotlib: A 2D graphics environment. Computing in science & engineering, 9(03), 90-95.
  • Hussain, N., Farooque, A. A., Schumann, A. W., McKenzie-Gopsill, A., Esau, T., Abbas, F., Acharya, B. & Zaman, Q. (2020). Design and development of a smart variable rate sprayer using deep learning. Remote Sensing, 12(24), 4091.
  • Leshkenov, A., & Shuganov, V. (2023). Resource-Saving Spraying Method Using the “Agroprotector-Robot”. In International Conference on Agriculture Digitalization and Organic Production (pp. 349-360). Singapore: Springer Nature Singapore.
  • Li, L., Gu, Q., Liu, L., 2020. Research on path planning algorithm for multi-UAV maritime targets search based on genetic algorithm. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 1, pp. 840-843). IEEE.
  • Li, W., Xia, L., Huang, Y., Mahmoodi, S., 2022. An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems. Journal of Ambient Intelligence and Humanized Computing, 1-15.
  • Manfreda, S., Eyal, B.D., 2023. Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. doi: 10.1016/C2020-0-02177-8.
  • Marzuki, O.F., Teo, E.Y.L., Rafie, A.S.M., 2021. The mechanism of drone seeding technology: a review. Malays. For, 84, 349-358.
  • Md, A.Q., Agrawal, D., Mehta, M., Sivaraman, A.K., Tee, K.F., 2021. Time optimization of unmanned aerial vehicles using an augmented path. Future Internet, 13(12), 308. doi: 10.3390/fi13120308.
  • Mukhamediev, R.I., Yakunin, K., Aubakirov, M., Assanov, I., Kuchin, Y., Symagulov, A., Levashenko, V., Zaitseva, E., Sokolov, D., Amirgaliyev, Y., 2023. Coverage path planning optimization of heterogeneous UAVs group for precision agriculture. IEEE Access, 11, 5789-5803. Doi: 10.1109/ACCESS.2023.3235207.
  • Niu, H., Ji, Z., Savvaris, A., Tsourdos, A., 2020. Energy efficient path planning for unmanned surface vehicle in spatially-temporally variant environment. Ocean Engineering, 196, 106766. doi: 10.1016/j.oceaneng.2019.106766.
  • Qu, C., Gai, W., Zhong, M., Zhang, J., 2020. A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied soft computing, 89, 106099. doi: 10.1016/j.asoc.2020.106099.
  • Pepe, M., Fregonese, L., & Scaioni, M. (2018). Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors. European Journal of Remote Sensing, 51(1), 412-436.
  • Rachmawati, S., Putra, A.S., Priyatama, A., Parulian, D., Katarina, D., Habibie, M.T., Siahaan, M., Ningrum, E. P., Medikano, A., Valentino, V.H., 2021. Application of drone technology for mapping and monitoring of corn agricultural land. In 2021 International Conference on ICT for Smart Society (ICISS) (pp. 1-5). IEEE. doi: 10.1109/ICISS53185.2021.9533231
  • Srivastava, K., Pandey, P.C., Sharma, J.K., 2020. An approach for route optimization in applications of precision agriculture using UAVs. Drones, 4(3), 58. doi: 10.3390/drones4030058.
  • Sundarraj, S., Reddy, R.V.K., Babu, B.M., Lokesh, G.H., Flammini, F., Natarajan, R., 2023. Route Planning for an Autonomous Robotic Vehicle Employing a Weight-Controlled Particle Swarm-Optimized Dijkstra Algorithm. IEEE Access. doi: 10.1109/ACCESS.2023.3302698.
  • Vazquez-Carmona, E.V., Vasquez-Gomez, J.I., Herrera-Lozada, J.C., Antonio-Cruz, M., 2022. Coverage path planning for spraying drones. Computers & Industrial Engineering, 168, 108125. doi: 10.48550/arXiv.2105.08743.
  • Wu, L., Huang, X., Cui, J., Liu, C., Xiao, W., 2023. Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot. Expert Systems with Applications, 215, 119410. doi: 10.1016/j.eswa.2022.119410.
  • Yan, C., Xiang, X., Wang, C., 2020. Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments. Journal of Intelligent & Robotic Systems, 98, 297-30
  • Yu, X., & Zhang, Y. (2015). Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects. Progress in Aerospace Sciences, 74, 152-166.
  • Zhai, L., Feng, S., 2022. A novel evacuation path planning method based on improved genetic algorithm. Journal of Intelligent & Fuzzy Systems, 42(3), 1813-1823. doi: 10.3233/JIFS-211214.
  • Zou, K., Wang, H., Zhang, F., Zhang, C., Kai, D., 2023. Precision route planning method based on UAV remote sensing and genetic algorithm for weeding machine. Applied Intelligence, 53(9), 11203-11213.

Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture

Year 2024, Volume: 39 Issue: 1, 129 - 142, 29.02.2024

Eray Önler

https://doi.org/10.7161/omuanajas.1394616

Abstract

In this study, the performance of the Genetic Algorithm (GA) in optimizing the agricultural drone flight route was compared with the Greedy Algorithm, revealing that GA produce routes that are, on average, 17.44 % more efficient. This efficiency, measured over 500 generations in a static field model, suggests substantial potential for saving resources and time in agricultural operations. Despite the effectiveness of the GA, its computational intensity limits real-time field applications, but offers advantages in offline route planning for pre-mapped areas. A t-test between flight lengths created by the algorithms highlighted a significant difference, with a p-value of approximately 7.18×10−9, indicating the GA's superior performance. Future research should aim to bridge the gap between the simplified binary field model used in simulations and the complexities of real-world agricultural landscapes to improve the practical deployment of GAs in drone route optimization.

Keywords

Agricultural drone drone route optimization offline route planning

References

  • Abdulsaheb, J.A., Kadhim, D.J., 2023. Classical and heuristic approaches for mobile robot path planning: A survey. Robotics, 12(4), 93. doi: 10.3390/robotics12040093.
  • Banpurkar, R., Raut, A.K., Ramteke, P.P., Prajapati, A.S., Sevaklal, A., Gautam, G.A.D., Bambole, A.S., 2021. Fertilizer Spraying UAV-A Review on Agriculture Drone.
  • Basiri, A., Mariani, V., Silano, G., Aatif, M., Iannelli, L., Glielmo, L., 2022. A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture. The Journal of Navigation, 75(2), 364-383. doi: 10.1017/S0373463321000825.
  • Cheikhrouhou, O., Khoufi, I., 2021. A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy. Computer Science Review, 40, 100369. doi: 10.1016/j.cosrev.2021.100369.
  • Chen, C.J., Huang, Y.Y., Li, Y.S., Chen, Y.C., Chang, C.Y., Huang, Y.M., 2021. Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access, 9, 21986-21997. doi: 10.1109/ACCESS.2021.3056082.
  • Delay, N.D., Thompson, N.M., Mintert, J.R., 2022. Precision agriculture technology adoption and technical efficiency. Journal of Agricultural Economics, 73(1), 195-219. doi: 10.1111/1477-9552.12440.
  • Edwards, C.A., 2020. The importance of integration in sustainable agricultural systems. In Sustainable agricultural systems (pp. 249-264). CRC Press
  • Fortin, F.A., De Rainville, F.M., Gardner, M.A.G., Parizeau, M., Gagné, C., 2012. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1), 2171-2175.
  • Hafeez, A., Husain, M.A., Singh, S.P., Chauhan, A., Khan, M.T., Kumar, N., Chauhan, A., Soni, S.K., 2022. Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture. doi: 10.1016/j.inpa.2022.02.002.
  • Hunter, J.D., 2007. Matplotlib: A 2D graphics environment. Computing in science & engineering, 9(03), 90-95.
  • Hussain, N., Farooque, A. A., Schumann, A. W., McKenzie-Gopsill, A., Esau, T., Abbas, F., Acharya, B. & Zaman, Q. (2020). Design and development of a smart variable rate sprayer using deep learning. Remote Sensing, 12(24), 4091.
  • Leshkenov, A., & Shuganov, V. (2023). Resource-Saving Spraying Method Using the “Agroprotector-Robot”. In International Conference on Agriculture Digitalization and Organic Production (pp. 349-360). Singapore: Springer Nature Singapore.
  • Li, L., Gu, Q., Liu, L., 2020. Research on path planning algorithm for multi-UAV maritime targets search based on genetic algorithm. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 1, pp. 840-843). IEEE.
  • Li, W., Xia, L., Huang, Y., Mahmoodi, S., 2022. An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems. Journal of Ambient Intelligence and Humanized Computing, 1-15.
  • Manfreda, S., Eyal, B.D., 2023. Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. doi: 10.1016/C2020-0-02177-8.
  • Marzuki, O.F., Teo, E.Y.L., Rafie, A.S.M., 2021. The mechanism of drone seeding technology: a review. Malays. For, 84, 349-358.
  • Md, A.Q., Agrawal, D., Mehta, M., Sivaraman, A.K., Tee, K.F., 2021. Time optimization of unmanned aerial vehicles using an augmented path. Future Internet, 13(12), 308. doi: 10.3390/fi13120308.
  • Mukhamediev, R.I., Yakunin, K., Aubakirov, M., Assanov, I., Kuchin, Y., Symagulov, A., Levashenko, V., Zaitseva, E., Sokolov, D., Amirgaliyev, Y., 2023. Coverage path planning optimization of heterogeneous UAVs group for precision agriculture. IEEE Access, 11, 5789-5803. Doi: 10.1109/ACCESS.2023.3235207.
  • Niu, H., Ji, Z., Savvaris, A., Tsourdos, A., 2020. Energy efficient path planning for unmanned surface vehicle in spatially-temporally variant environment. Ocean Engineering, 196, 106766. doi: 10.1016/j.oceaneng.2019.106766.
  • Qu, C., Gai, W., Zhong, M., Zhang, J., 2020. A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied soft computing, 89, 106099. doi: 10.1016/j.asoc.2020.106099.
  • Pepe, M., Fregonese, L., & Scaioni, M. (2018). Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors. European Journal of Remote Sensing, 51(1), 412-436.
  • Rachmawati, S., Putra, A.S., Priyatama, A., Parulian, D., Katarina, D., Habibie, M.T., Siahaan, M., Ningrum, E. P., Medikano, A., Valentino, V.H., 2021. Application of drone technology for mapping and monitoring of corn agricultural land. In 2021 International Conference on ICT for Smart Society (ICISS) (pp. 1-5). IEEE. doi: 10.1109/ICISS53185.2021.9533231
  • Srivastava, K., Pandey, P.C., Sharma, J.K., 2020. An approach for route optimization in applications of precision agriculture using UAVs. Drones, 4(3), 58. doi: 10.3390/drones4030058.
  • Sundarraj, S., Reddy, R.V.K., Babu, B.M., Lokesh, G.H., Flammini, F., Natarajan, R., 2023. Route Planning for an Autonomous Robotic Vehicle Employing a Weight-Controlled Particle Swarm-Optimized Dijkstra Algorithm. IEEE Access. doi: 10.1109/ACCESS.2023.3302698.
  • Vazquez-Carmona, E.V., Vasquez-Gomez, J.I., Herrera-Lozada, J.C., Antonio-Cruz, M., 2022. Coverage path planning for spraying drones. Computers & Industrial Engineering, 168, 108125. doi: 10.48550/arXiv.2105.08743.
  • Wu, L., Huang, X., Cui, J., Liu, C., Xiao, W., 2023. Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot. Expert Systems with Applications, 215, 119410. doi: 10.1016/j.eswa.2022.119410.
  • Yan, C., Xiang, X., Wang, C., 2020. Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments. Journal of Intelligent & Robotic Systems, 98, 297-30
  • Yu, X., & Zhang, Y. (2015). Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects. Progress in Aerospace Sciences, 74, 152-166.
  • Zhai, L., Feng, S., 2022. A novel evacuation path planning method based on improved genetic algorithm. Journal of Intelligent & Fuzzy Systems, 42(3), 1813-1823. doi: 10.3233/JIFS-211214.
  • Zou, K., Wang, H., Zhang, F., Zhang, C., Kai, D., 2023. Precision route planning method based on UAV remote sensing and genetic algorithm for weeding machine. Applied Intelligence, 53(9), 11203-11213.
There are 30 citations in total.

Details

Primary Language English
Subjects Biosystem
Journal Section Anadolu Tarım Bilimleri Dergisi
Authors

Eray Önler 0000-0001-7700-3742

Early Pub Date February 27, 2024
Publication Date February 29, 2024
Submission Date November 22, 2023
Acceptance Date December 28, 2023
Published in Issue Year 2024 Volume: 39 Issue: 1

Cite

APA Önler, E. (2024). Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture. Anadolu Tarım Bilimleri Dergisi, 39(1), 129-142. https://doi.org/10.7161/omuanajas.1394616
AMA Önler E. Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture. ANAJAS. February 2024;39(1):129-142. doi:10.7161/omuanajas.1394616
Chicago Önler, Eray. “Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture”. Anadolu Tarım Bilimleri Dergisi 39, no. 1 (February 2024): 129-42. https://doi.org/10.7161/omuanajas.1394616.
EndNote Önler E (February 1, 2024) Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture. Anadolu Tarım Bilimleri Dergisi 39 1 129–142.
IEEE E. Önler, “Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture”, ANAJAS, vol. 39, no. 1, pp. 129–142, 2024, doi: 10.7161/omuanajas.1394616.
ISNAD Önler, Eray. “Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture”. Anadolu Tarım Bilimleri Dergisi 39/1 (February 2024), 129-142. https://doi.org/10.7161/omuanajas.1394616.
JAMA Önler E. Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture. ANAJAS. 2024;39:129–142.
MLA Önler, Eray. “Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture”. Anadolu Tarım Bilimleri Dergisi, vol. 39, no. 1, 2024, pp. 129-42, doi:10.7161/omuanajas.1394616.
Vancouver Önler E. Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture. ANAJAS. 2024;39(1):129-42.
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