Criteria for the Configuration of Augmented Intelligence Platforms for Improving Agricultural Crop Sustainability
DOI:
https://doi.org/10.26439/ciis2020.5516Keywords:
augmented intelligence, multispectral image, machine & deep learnin, IoT-IoB networks, operational riskAbstract
The development of artificial intelligence has posed several challenges regar ding the future and sustainability of human work. However, the development of technology has brought concepts such as augmented intelligence (AI), which aims to improve human capabilities through human-machine interaction to solve complex problems in different areas of knowledge. This interaction entails a series of technological challenges since the human experience is a complex transfer learning process in which machines are a perfect complement to people. In the context of precision agriculture, AI platforms (AIPs) have emerged as an important alternative to strengthen capacities for the detection and diagnosis of phytosanitary or agroclimatic conditions. This article proposes a methodology for the configuration of AIPs by integrating three fundamental elements for the sustainability of crops: spectral aerial images using unmanned aerial vehicles (UAVs), forecast maps to describe the spread of diseases and their associated vectors in the field, deep & machine learning models for the automatic charac terization of phytosanitary or agroclimatic events, and IoT-IoB (Internet of Things & Internet of Beings) networks for human-device interaction. For the evaluation of these platforms, a sustainability gap is proposed, which comprehensively assesses the reduction in pesticide and fertilizer use, as well as the sustainability of jobs in the long-term future where artificial intelli gence will play a leading role in the agricultural development in the world.
Downloads
References
Aleaur, S. (s. f.). Building a Sustainable Future Together! https://www.sulitest.org
Anderson, R. (Director). (2009). The Business Logic of Sustainability [Película].Anthropocene. (24 de enero del 2021). En Wikipedia. https://en.wikipedia.org/wiki/Anthropocene
Baseca, C., Sendra, S., Lloret, J., y Tomas, J. (2019). A Smart Decision System for Digital Farming. Agronomy, 9(5). https://10.3390/agronomy9050216
Broman, G. I., y Robèrt, K.-H. (2017). A framework for strategic sustainable development. Journal of Cleaner Production, 140, 17-31.
Castrignano, A., Buttafuoco, G., Khosla, R., Mouazen, A., Moshou, D., y Naud, O. (2020). Agricultural Internet of Things and Decision Support for Precision Smart Farming. Elsevier. doi: ISBN 9780128183731
Corley, R. H. V., y Tinker, B. (2015). The Oil Palm (Fifth edition). Wiley Online Library.
Díaz, J. (2015). Estudio de índices de vegetación a partir de imágenes aéreas tomadas desde UAS/RPAS y aplicaciones de estos a la agricultura de precisión. Universidad Complutense de Madrid.
Fitrianto, A., Darmawan, A., Tokimatsu, K., y Sufwandika, M. (2018). Estimating the Age of Oil Palm Trees Using Remote Sensing Technique. IOP Conference Series: Earth and Environmental Science, 148, 012020. https://10.1088/1755-1315/148/1/012020
Giraldo, R. (2016). Estudio de firmas espectrales de palmas de aceite afectadas con la marchitez letal, usando análisis estadísticos de datos funcionales. Palmas, 37, 131-139. https://publicaciones.fedepalma.org/index.php/palmas/article/view/11897
Global Footprint Network. (s. f.). Earth Overshoot Day. https://www.footprintnetwork.org/our-work/earth-overshoot-day/
Kath, J., Mushtag, S., Henry, R., Adewuyi, A., y Stone, R. (2018). Index Insurance Benefits Agricultural Producers Exposed to Excessive Rainfall Risk. Weather and Climate Extremes, 22, 1149-1159. https://doi.org/10.1016/j.wace.2018.10.003
Kathun, R., Reza, M., Moniruzzaman, M., y Yaakob, Z. (2017). Sustainable Oil Palm Industry: The Possibilities. Renewable and Sustainable Energ y Reviews, 76, 608-619. https://doi.org/10.1016/j.rser.2017.03.077
Lai, O. M., Tan, C. P., y Akoh, C. C. (2015). Palm Oil: Production, Processing, Characterization, and Uses. Elsevier Science. https://doi.org/10.1016/C2015-0-02411-X
Liu, G., Bao, H., y Baokun, H. (2018). A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis. Mathematical Problems in Engineering, 1-10. https://10.1155/2018/5105709
Max-Neef, M., Elizalde, A., y Hopenhayn, M. (1991). Human Scale Development: Conception, Application and Further Reflections. Zed Books.
Mohanty, S., Hughes, D., y Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419-1429. https://10.3389/fpls.2016.01419
Molina, V., Acosta, M., Torres, J., y Hernández, J. (Octubre del 2019). Diferencias entre el comportamiento espectral de palmas sanas y palmas afectadas por marchitez letal (ML). ResearchGate. https://www.researchgate.net/publication/336197147_Diferencias_entre_el_comportamiento_espectral_de_palmas_sanas_y_palmas_afectadas_por_Marchitez_letal_ML
Oppenheimer, A. (2018). ¡Sálvese quien pueda! El futuro del trabajo en la era de la auto-matización. Debate.
Peña, A., Bonet, I., Lochmuller, C., Patiño, H., Chiclana, F., y Góngora, M. (2018). A Fuzzy Credibility Model to Estimate the Operational Value at Risk Using Internal and External Data of Risk Events. Knowledge-Based Systems, 159, 98-109. https://doi.org/10.1016/j.knosys.2018.06.007
Peña, A., Bonet, I., Manzur, D., Góngora, M., y Carffini, F. (2019). Validation of Convolutional Layers in Deep Learning Models to Identify Patterns in Multispectral Images. Proceedings of 14th. Conference on Informations Systems and Technologies (pp. 1-6). Systems Information and Technologies Association (AISTI). https://doi.org/10.23919/CISTI.2019.8760741
Peña, A., Bonet, I., Lochmuller, C., Chiclana, F., y Góngora, M. (2018). Flexible Inverse Adaptive Fuzzy Inference Model to Identify the Evolution of Operational Value at Risk for Improving Operational Risk Management. Applied Soft Computing, 65, 614-631. https://doi.org/10.1016/j.asoc.2018.01.024
Peña, A., Hernández, J., y Toro, M. (2010). Computational Evolutionary Inverse Lagrangian Puff Model. Environmental Modelling y Software, 25(12), 1890-1893. http://www.sciencedirect.com/science/article/pii/S1364815210001192
Popovic, T., Nedeljko, L., Pesic, A., Zecevic, Z., Krstajic, B., y Djukanovik, S. (2017). Architecting an IoT-Enabled Platform for Precision Agriculture and Ecological Monitoring : A Case Study. Computers and Electronics in Agriculture, 140, 255-265. https://doi.org/10.1016/j.compag.2017.06.008
Richard, R., y Lundholm, K. (2005). Engaging Individuals to Act Strategically Towards Sustainability. School of Engineering Blekinge Institute of Technology.
RSPO. (2013). RSPO Principles and Criteria for the Production of Sustainable Palm Oil. http://www.rspo.org/key-documents/certification/rspo-principles-and-criteria
Shirsath, P., Vyas, S., Aggraval, P., y Kolli, R. (2019). Designing Weather Index Insurance of Crops for the Increased Satisfaction of Farmers, Industry and the Government. Climate Risk Management, 25. https://doi.org/10.1016/j.crm.2019.100189
Sivers, D. (Director). (2010). How to start a movement [Película].
Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I., Bennett, E. M., Biggs, R., Carpenter, S. R., de Vries, W., de Wit, C. A., Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan, V., Reyers, B., y Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223). https://doi.org/10.1126/science.1259855
Sulitest. (2020). Raising y Mapping Awareness of the Global Goals. United Nations virtual edition. https://www.sulitest.org/hlpf2020report.pdf.
Talaviya, T., Shah, D., Patel, N., Yagnik, H., y Shah, M. (2020). Implementation of Artificial Intelligence in Agriculture for Optimisation of Irrigation and Application of Pesticides and Herbicides. Artificial Intelligence in Agriculture, 4, 58-73. https://doi.org/10.1016/j.aiia.2020.04.002
Trenca, I., Pece, A. M., y Mihuţ, I. S. (2015). The Assessment of Market Risk in the Context of the Current Financial Crisis. Procedia Economics and Finance, 32, 1391-1406. https://doi.org/10.1016/S2212-5671(15)01516-6
United Nations Department of Economic and Social Affairs. (s. f.). The 17 Goals Sustainable Development. https://sdgs.un.org/goals
World Commission on Environment and Development. (1987). Our Common Future. Oxford University Press.
Zhao, S., Dong, Y., y Ying, H. (2020). The Reliability Analysis of Rating Systems in Decision Making: When Scale Meets Multi-Attribute Additive Value Model. Decision Support Systems, 138. https://doi.org/10.1016/j.dss.2020.113384
