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Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms

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Metaheuristics (MIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13838))

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Abstract

Precision agriculture is a research area that uses technology from engineering and computer science to improve all aspects of agriculture, including but not limited to crop health, irrigation, and fertilizer application. In agriculture, questions of sustainability often arise: How do we minimize environmental impact while simultaneously helping farmers maximize their net return? In this paper, we present a method to optimize crop yield production in winter wheat, with the goal of seeking to increase farmers’ production. However, only focusing on optimizing production can lead to poor sustainability if an unnecessary amount of fertilizer is applied or farming equipment is put under undo stress. We therefore seek to address these impacts on sustainability by including objectives that directly address these concerns. Our method utilizes a new approach to solve multi-objective optimization that uses overlapping subpopulations, known as a Multi-Objective Factored Evolutionary Algorithm. Our results indicate that including overlapping subpopulations in the multi-objective optimization context is beneficial for exploration of the objective space. Our results also indicate that including these sustainability-driven objectives does not significantly impact net return or yield.

Supported by NSF grant 1658971 and USDA Grant NR213A750013G021.

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References

  1. Anonymous, A.: Reduced-cost hyperspectral convolutional neural networks. J. Appl. Remote Sens. 14(3), 036519–036519 (2020)

    Google Scholar 

  2. Bongiovanni, R., Lowenberg-Deboer, J.: Precision agriculture and sustainability. Precis. Agric. 5(4), 359–387 (2004)

    Article  Google Scholar 

  3. Bringmann, K., Friedrich, T.: Approximation quality of the hypervolume indicator. Artif. Intell. 195, 265–290 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Burk, F.: Lebesgue Measure and Integration: An Introduction. John Wiley, Hoboken (2011)

    MATH  Google Scholar 

  5. De Koeijer, T., Wossink, G., Verhees, F.: Environmental and economic effects of spatial variability in cropping: nitrogen fertilization and site-specific management. In: The Economics of Agro-Chemicals, pp. 187–200 (2018)

    Google Scholar 

  6. Deb, K.: Multi-objective optimization. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 403–449. Springer, Boston (2014). https://doi.org/10.1007/978-1-4614-6940-7_15

    Chapter  Google Scholar 

  7. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evol. Comp. 18(4), 577–601 (2013)

    Article  Google Scholar 

  8. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comp. 6(2), 182–197 (2002)

    Article  Google Scholar 

  9. Ehrgott, M., Gandibleux, X.: A survey and annotated bibliography of multiobjective combinatorial optimization. OR-Spektrum 22(4), 425–460 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  10. Engelbrecht, A.P.: Fitness function evaluations: a fair stopping condition? In: 2014 IEEE Symposium on Swarm Intelligence, pp. 1–8 (2014)

    Google Scholar 

  11. Fortier, N., Sheppard, J., Strasser, S.: Abductive inference in Bayesian networks using distributed overlapping swarm intelligence. Soft. Comput. 19(4), 981–1001 (2015)

    Article  Google Scholar 

  12. Goh, C., Tan, K., Liu, D., Chiam, S.: A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. Eur. J. Oper. Res. 202, 42–54 (2010)

    Article  MATH  Google Scholar 

  13. Ishibuchi, H., Imada, R., Setoguchi, Y., Nojima, Y.: Performance comparison of NSGA-II and NSGA-III on various many-objective test problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3045–3052. IEEE (2016)

    Google Scholar 

  14. Ishibuchi, H., Shibata, Y.: Mating scheme for controlling the diversity-convergence balance for multiobjective optimization. In: GECCO, pp. 1259–1271 (2004)

    Google Scholar 

  15. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: IEEE CEC, pp. 2419–2426 (2008)

    Google Scholar 

  16. Jeong, J.H., et al.: Random forests for global and regional crop yield predictions. Public Libr. Sci. 11(6), e0156571 (2016)

    Google Scholar 

  17. Kim, S., Dale, B.E.: Effects of nitrogen fertilizer application on greenhouse gas emissions and economics of corn production. Environ. Sci. Technol. 42(16), 6028–6033 (2008)

    Article  Google Scholar 

  18. Lehmann, N., Finger, R.: Optimizing whole-farm management considering price and climate risks. In: 123rd European Association of Agricultural Economists Seminar (2012)

    Google Scholar 

  19. Maneeratana, K., Boonlong, K., Chaiyaratana, N.: Multi-objective optimisation by co-operative co-evolution. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 772–781. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_78

    Chapter  Google Scholar 

  20. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  21. Peerlinck, A., Sheppard, J., Maxwell, B.: Using deep learning in yield and protein prediction of winter wheat based on fertilization prescriptions in precision agriculture. In: International Conference on Precision Agriculture (2018)

    Google Scholar 

  22. Peerlinck, A., Sheppard, J., Pastorino, J., Maxwell, B.: Optimal design of experiments for precision agriculture using a genetic algorithm. In: IEEE CEC, pp. 1838–1845 (2019)

    Google Scholar 

  23. Potter, M.A., Jong, K.A.D.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)

    Article  Google Scholar 

  24. Pryor, E., Peerlinck, A., Sheppard, J.: A study in overlapping factor decomposition for cooperative co-evolution. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 01–08. IEEE (2021)

    Google Scholar 

  25. Raun, W.R., et al.: Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94(4), 815–820 (2002)

    Article  Google Scholar 

  26. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  27. Strasser, S., Sheppard, J., Fortier, N., Goodman, R.: Factored evolutionary algorithms. IEEE Trans. Evol. Comp. 21(2), 281–293 (2017)

    Article  Google Scholar 

  28. Thrikawala, S., Weersink, A., Fox, G., Kachanoski, G.: Economic feasibility of variable-rate technology for nitrogen on corn. Am. J. Agric. Econ. 81(4), 914–927 (1999)

    Article  Google Scholar 

  29. United States Department of Agriculture: Agricultural prices (2022). https://usda.library.cornell.edu/concern/publications/c821gj76b?locale=en

  30. U.S. Congress: Agricultural research, extension, and teaching. In: U.S. Code Title 7, chap. 64. No. 3103, U.S. Government Publishing Office, Washington, DC, USA (2011). https://www.gpo.gov/

  31. Van Klompenburg, T., Kassahun, A., Catal, C.: Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020)

    Article  Google Scholar 

  32. Whitley, K.M., Davenport, J.R., Manley, S.R.: Differences in nitrate leaching under variable and conventional nitrogen fertilizer management in irrigated potato systems. In: Robert, P.C., Rust, R.H., Larson, W.E. (eds.) Proceedings of the 5th International Conference on Precision Agriculture, pp. 1–9. American Society of Agronomy, Madison (2000)

    Google Scholar 

  33. Yu, X., et al.: Set-based discrete particle swarm optimization based on decomposition for permutation-based multiobjective combinatorial optimization problems. IEEE Trans. Cybern. 48(7), 2139–2153 (2017)

    Article  Google Scholar 

  34. Zheng, Y.J., Song, Q., Chen, S.Y.: Multiobjective fireworks optimization for variable-rate fertilization in oil crop production. Appl. Soft Comput. 13(11), 4253–4263 (2013)

    Article  Google Scholar 

  35. Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Ph.D. thesis, Swiss Federal Institute of Technology (1999)

    Google Scholar 

  36. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comp. 8(2), 173–195 (2000)

    Article  Google Scholar 

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Authors and Affiliations

  1. Montana State University, Bozeman, MT, USA

    Amy Peerlinck & John Sheppard

Authors
  1. Amy Peerlinck
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  2. John Sheppard
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Correspondence to Amy Peerlinck .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Luca Di Gaspero

  2. University of Napoli - Federico II, Napoli, Italy

    Paola Festa

  3. University of Paris Est, Paris, France

    Amir Nakib

  4. University of Catania, Catania, Italy

    Mario Pavone

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Peerlinck, A., Sheppard, J. (2023). Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-26504-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26503-7

  • Online ISBN: 978-3-031-26504-4

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