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

Yıl 2024, Cilt: 39 Sayı: 1, 129 - 142, 29.02.2024

Eray Önler

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

Öz

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.

Anahtar Kelimeler

Dron rota optimizasyonu tarımsal dron çevrimdışı rota planlama

Kaynakça

  • 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.

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

Yıl 2024, Cilt: 39 Sayı: 1, 129 - 142, 29.02.2024

Eray Önler

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

Öz

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.

Anahtar Kelimeler

Agricultural drone drone route optimization offline route planning

Kaynakça

  • 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.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyosistem
Bölüm Anadolu Tarım Bilimleri Dergisi
Yazarlar

Eray Önler 0000-0001-7700-3742

Erken Görünüm Tarihi 27 Şubat 2024
Yayımlanma Tarihi 29 Şubat 2024
Gönderilme Tarihi 22 Kasım 2023
Kabul Tarihi 28 Aralık 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 39 Sayı: 1

Kaynak Göster

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. Şubat 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, sy. 1 (Şubat 2024): 129-42. https://doi.org/10.7161/omuanajas.1394616.
EndNote Önler E (01 Şubat 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, c. 39, sy. 1, ss. 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 (Şubat 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, c. 39, sy. 1, 2024, ss. 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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Online ISSN: 1308-8769