Abstract
Agriculture plays an essential role in sustaining human activities. Challenges such as the indiscriminate use of pesticides pose a threat to food security. Evolutionary computing (EC) has emerged as a robust computational methodology for the treatment of many complex agricultural problems in recent years. In addition, scientific workflows are a technology that supports the automation and reproducibility of large-scale in silico experiments. However, the design of evolutionary workflows is still an open issue for decision-makers. Therefore, to bridge this gap, we present a novel approach to help researchers model evolutionary workflows. To answer this question, in this chapter, we use VisPyGMO, which offers a set of evolutionary algorithm modules that help researchers build reusable evolutionary workflows more efficiently. Moreover, we show the feasibility of VisPyGMO in analysing a large real-world agricultural dataset used to respond to competency questions (CQ) and predict future use of pesticides.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abramson, D., Bethwaite, B., Enticott, C., Garic, S., Peachey, T., Michailova, A., et al. (2010). Embedding optimization in computational science workflows. Journal of Computational Science, 1, 41–47. https://doi.org/10.1016/j.jocs.2010.04.002
Allison, D. B., Shiffrin, R. M., & Stodden, V. (2018). Reproducibility of research: Issues and proposed remedies. Proceedings of the National Academy of Sciences of the United States of America, 115, 2561–2562. https://doi.org/10.1073/pnas.1802324115
Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., et al. (2019). A survey on evolutionary machine learning. Journal of the Royal Society of New Zealand, 49, 205–228. https://doi.org/10.1080/03036758.2019.1609052
Azzaoui, K., Jacoby, E., Senger, S., RodrÃguez, E. C., Loza, M., Zdrazil, B., et al. (2013). Scientific competency questions as the basis for semantically enriched open pharmacological space development. Drug Discovery Today, 18, 843–852. https://doi.org/10.1016/j.drudis.2013.05.008
Baeck, T., Fogel, D. B., & Michalewicz, Z. (1997). Handbook of evolutionary computation (1st ed.). Thomas Baeck - D. CRC Press. 1130-undefined. Retrieved April 22, 2021, from https://www.routledge.com/Handbook-of-Evolutionary-Computation/Baeck-Fogel-Michalewicz/p/book/9780750308953
Baker, M., & Penny, D. (2016). Is there a reproducibility crisis? Nature, 533, 452–454. https://doi.org/10.1038/533452A
Bartz-Beielstein, T. (2006). Research in evolutionary computation. In Exp. Res. Evol. Comput. (pp. 3–12). Springer-Verlag. https://doi.org/10.1007/3-540-32027-x_1
Bartz-Beielstein, T., & Preuss, M. (2009). The future of experimental research. In Proc. 11th Annu. Conf. companion Genet. Evol. Comput. Conf. - GECCO ’09 (p. 3185). Association for Computing Machinery (ACM). https://doi.org/10.1145/1570256.1570417
Benureau, F. C. Y., & Rougier, N. P. (2018). Re-run, repeat, reproduce, reuse, replicate: Transforming code into scientific contributions. Frontiers in Neuroinformatics, 11, 69. https://doi.org/10.3389/fninf.2017.00069
Callahan, S. P., Freire, J., Santos, E., Scheidegger, C. E., Silva, C. T., & Vo, H. T. (2006). VisTrails: Visualization meets data management. In Proc. ACM SIGMOD Int. Conf. Manag. Data (pp. 745–747). ACM Press. https://doi.org/10.1145/1142473.1142574
Cheng, S., Liu, B., Shi, Y., Jin, Y., & Li, B. (2016). Evolutionary computation and big data: Key challenges and future directions. In Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) (Vol. 9714, pp. 3–14). LNCS. https://doi.org/10.1007/978-3-319-40973-3_1
Coello Coello, C., Lamont, G. B., & van Veldhuizen, D. A. (2007). Basic concepts. In Evol. Algorithms Solving Multi-objective Probl. (pp. 1–60). Springer US. https://doi.org/10.1007/978-0-387-36797-2_1
Crepinsek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys, 45. https://doi.org/10.1145/2480741.2480752
Crick, T., Dunning, P., Kim, H., & Padget, J. (2009). Engineering design optimization using services and workflows. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367, 2741–2751. https://doi.org/10.1098/rsta.2009.0035
Cruz, S. M. S., Ceddia, M. B., Schmitz, E. A., Rizzo, G. S., Miranda, R. C. T., Cruz, S. O., Correa, A. C., Klinger, F., Marinho, E. C., & Cruz, P. V. (2018). Towards an e-infrastructure for Open Science in Soils Security. Anais Do Brazilian E-Science Workshop (BreSci). https://doi.org/10.5753/bresci.2018.3273
Da Cruz, S. M. S., Campos, M. L. M., & Mattoso, M. (2009). Towards a taxonomy of provenance in Scientific Workflow Management Systems. In Serv. 2009 - 5th 2009 World Congr. Serv. (pp. 259–266). https://doi.org/10.1109/SERVICES-I.2009.18
Da Cruz, S. M. S., De Oliveira, A., & Firmino De Faria, F. (2018). Evolutionary scientific workflows. In 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc. Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477850
Dalal, S. R., Shekelle, P. G., Hempel, S., Newberry, S. J., Motala, A., & Shetty, K. D. (2013). A pilot study using machine learning and domain knowledge to facilitate comparative effectiveness review updating. Medical Decision Making, 33, 343–355. https://doi.org/10.1177/0272989X12457243
Deelman, E., Gannon, D., Shields, M., & Taylor, I. (2009). Workflows and e-Science: An overview of workflow system features and capabilities. Future Generation Computer Systems, 25, 528–540. https://doi.org/10.1016/j.future.2008.06.012
Deelman, E., Singh, G., Su, M. H., Blythe, J., Gil, Y., Kesselman, C., et al. (2005). Pegasus: A framework for mapping complex scientific workflows onto distributed systems. Scientific Programming, 13, 219–237. https://doi.org/10.1155/2005/128026
Drummond, C. (2009). Replicability is not reproducibility: nor is it good science. Undefined.
Eiben, A. E., & Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521, 476–482. https://doi.org/10.1038/nature14544
Eldredge, N., & Gould, S. J. (1988). Punctuated equilibrium prevails [5]. Nature, 332, 211. https://doi.org/10.1038/332211b0
FAO. (2009). Submission and evaluation of pesticide residues data for the estimation of maximum residue levels in food and feed. FOOD Agric Organ UNITED NATIONS 2009. Retrieved April 23, 2021, from http://www.fao.org/3/i1216e/i1216e00.htm
Fernández de Vega, F., Hidalgo Pérez, J. I., & Lanchares, J. (Eds.). (2012). Parallel architectures and bioinspired algorithms (Vol. 415). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-28789-3
Goodman, S. N., Fanelli, D., & Ioannidis, J. P. A. (2018). What does research reproducibility mean? In Get. to Good Res. Integr. Biomed. Sci. (Vol. 8, pp. 96–102). Springer International Publishing. https://doi.org/10.1126/scitranslmed.aaf5027
Habib, I., Anjum, A., McClatchey, R., & Rana, O. (2013). Adapting scientific workflow structures using multi-objective optimization strategies. ACM Transactions on Autonomous and Adaptive Systems, 8, 1–21. https://doi.org/10.1145/2451248.2451252
Holland, J. H. (1992). Adaptation in natural and artificial systems. MIT Press. Retrieved April 23, 2021, from https://mitpress.mit.edu/books/adaptation-natural-and-artificial-systems
Hyndman, R. J., & Athanasopoulos, G. (2013). Forecasting: Principles and practice (3rd ed.). OText. Retrieved April 23, 2021, from https://otexts.com/fpp3/
Izzo, D., Ruciński, M., & Ampatzis, C. (2009). Parallel global optimisation meta-heuristics using an asynchronous island-model. In 2009 IEEE Congr. Evol. Comput. CEC 2009 (pp. 2301–2308). https://doi.org/10.1109/CEC.2009.4983227
Izzo, D., Ruciński, M., & Biscani, F. (2012). The generalized Island model. Studies in Computational Intelligence, 415, 151–169. https://doi.org/10.1007/978-3-642-28789-3_7
Kaufmann, P., & Castillo, P. A. (Eds.). (2019). Applications of evolutionary computation (Vol. 11454). Springer International Publishing. https://doi.org/10.1007/978-3-030-16692-2
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proc. ICNN’95 - Int. Conf. Neural Networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680. https://doi.org/10.1126/science.220.4598.671
Leveque, R., Mitchell, I., & Stodden, V. (2012). Reproducible research for scientific computing: Tools and strategies for changing the culture. Computing in Science & Engineering, 14, 13–17. https://doi.org/10.1109/MCSE.2012.38
Maier, H. R., Razavi, S., Kapelan, Z., Matott, L. S., Kasprzyk, J., & Tolson, B. A. (2019). Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. Environmental Modelling and Software, 114, 195–213. https://doi.org/10.1016/j.envsoft.2018.11.018
Manninen, T., Aćimović, J., Havela, R., Teppola, H., & Linne, M. L. (2018). Challenges in reproducibility, replicability, and comparability of computational models and tools for neuronal and glial networks, cells, and subcellular structures. Frontiers in Neuroinformatics, 12, 20. https://doi.org/10.3389/fninf.2018.00020
Märtens, M., & Izzo, D. (2013). The asynchronous island model and NSGA-II: Study of a new migration operator and its performance. In GECCO 2013 - Proc. 2013 Genet. Evol. Comput. Conf. (pp. 1173–1180). ACM Press. https://doi.org/10.1145/2463372.2463516
Mattoso, M., Werner, C., Travassos, G. H., Braganholo, V., Ogasawara, E., De Oliveira, D., et al. (2010). Towards supporting the life cycle of large scale scientific experiments. International Journal of Business Process Integration and Management, 5, 79–92. https://doi.org/10.1504/IJBPIM.2010.033176
Mauttone, A., & Plà -Aragonés, L. M. (2022). Preface: Contributions of OR to solve agricultural problems. Annals of Operations Research, 314, 317–318. https://doi.org/10.1007/s10479-022-04791-4
McDougal, R. A., Bulanova, A. S., & Lytton, W. W. (2016). Reproducibility in computational neuroscience models and simulations. IEEE Transactions on Biomedical Engineering, 63, 2021–2035. https://doi.org/10.1109/TBME.2016.2539602
Monajemi, H., Murri, R., Jonas, E., Liang, P., Stodden, V., & Donoho, D. (2019). Ambitious data science can be painless. Harvard Data Science Review, 1. https://doi.org/10.1162/99608f92.02ffc552
Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie Du Sert, N., et al. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1, 1–9. https://doi.org/10.1038/s41562-016-0021
National Academies of Sciences E and M. (2019). Reproducibility and replicability in science. National Academies Press. https://doi.org/10.17226/25303
Nguyen, H. A., Van Iperen, Z., Raghunath, S., Abramson, D., Kipouros, T., & Somasekharan, S. (2017). Multi-objective optimisation in scientific workflow. In Procedia Comput. Sci. (Vol. 108, pp. 1443–1452). Elsevier B.V.. https://doi.org/10.1016/j.procs.2017.05.213
Plà -Aragonés, L. M. (Ed.). (2015). Handbook of operations research in agriculture and the agri-food industry (Vol. 224). Springer New York. https://doi.org/10.1007/978-1-4939-2483-7
Prabhu, P., Kim, H., Oh, T., Jablin, T. B., Johnson, N. P., Zoufaly, M., et al. (2011). A survey of the practice of computational science. In State Pract. Reports, SC’11 (p. 1). ACM Press. https://doi.org/10.1145/2063348.2063374
PyGMO. (2021). Welcome to PyGMO — PyGMO 1.1.7dev documentation 2021. Retrieved April 23, 2021, from https://esa.github.io/pygmo/
Rougier, N. P., Hinsen, K., Alexandre, F., Arildsen, T., Barba, L. A., Benureau, A. C. Y., et al. (2017). Sustainable computational science: The ReScience Initiative. PeerJ Computer Science, 2017, e142. https://doi.org/10.7717/peerj-cs.142
Ruciński, M., Izzo, D., & Biscani, F. (2010). On the impact of the migration topology on the Island Model. Parallel Computing, 36, 555–571. https://doi.org/10.1016/j.parco.2010.04.002
Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLoS Computational Biology, 9, e1003285. https://doi.org/10.1371/journal.pcbi.1003285
Sharma, A., Kumar, V., Shahzad, B., Tanveer, M., Sidhu, G. P. S., Handa, N., et al. (2019). Worldwide pesticide usage and its impacts on ecosystem. SN Applied Sciences, 1, 1–16. https://doi.org/10.1007/s42452-019-1485-1
Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4, 41–49. https://doi.org/10.1016/j.inpa.2016.10.005
Smith, S. L. (2015). Medical applications of evolutionary computation. In GECCO 2015 - Companion Publ. 2015 Genet. Evol. Comput. Conf. (pp. 651–679). Association for Computing Machinery, Inc. https://doi.org/10.1145/2739482.2756567
Soto-Silva, W. E., Nadal-Roig, E., Ganzález-Araya, M. C., & Plà -Aragones, L. M. (2016). Operational research models applied to the fresh fruit supply chain. European Journal of Operational Research, 251(2), 345–355. https://doi.org/10.1016/j.ejor.2015.08.046
Tang, F. H. M., Lenzen, M., McBratney, A., & Maggi, F. (2021). Risk of pesticide pollution at the global scale. Nature Geoscience, 14, 206–210. https://doi.org/10.1038/s41561-021-00712-5
Tauritz, D. (2009). Grand challenges in evolutionary computing: Part I.
USDA. (2021). PDP Databases and Annual Summaries | Agricultural Marketing Service 2021. Retrieved April 23, 2021, from https://www.ams.usda.gov/datasets/pdp/pdpdata
Wilkinson, M. D., Dumontier, M., IJ, A., Appleton, G., Axton, M., Baak, A., et al. (2016). Comment: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 1–9. https://doi.org/10.1038/sdata.2016.18
Yu, H., Yang, L., Li, D., & Chen, Y. (2021). A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture. Information Processing in Agriculture, 8, 64–74. https://doi.org/10.1016/j.inpa.2020.04.002
Acknowledgments
This study was partially funded in part by the Coordenação de Aperfeiçoamento de Pessoal de Nvel Superior – Brazil (CAPES) – Finance Code 001 and partially sponsored by the National Council of Scientific and Technological Development (CNPq) – Grant DT II (315399/2018-0, 306115/2021-2) and project (400044/2023-4), Brazilian National Fund to Develop Education (FNDE), Educational Tutorial Programme (PET-SI/UFRRJ), and Carlos Chagas Filho Research Foundation (FAPERJ). We thank the BigDSSAgro CYTED network.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Oliveira, A., Firmino, F., Cruz, P.V., de Oliveira Sampaio, J., da Cruz, S.M.S. (2024). In Silico Evaluation and Prediction of Pesticide Supported by Reproducible Evolutionary Workflows. In: Albornoz, V.M., Mac Cawley, A., Plà -Aragonés, L.M. (eds) Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry. Springer, Cham. https://doi.org/10.1007/978-3-031-49740-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-49740-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49739-1
Online ISBN: 978-3-031-49740-7
eBook Packages: Business and ManagementBusiness and Management (R0)