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Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

Author

Listed:
  • Alejandro Pena

    (Departamento de Contaduría, Escuela de Administración, Grupo de Investigación en Información y Gestión, Universidad EAFIT, Medellín 050022, Colombia)

  • Juan C. Tejada

    (Computational Intelligence and Automation Research Group, Universidad EIA, Envigado 055413, Colombia)

  • Juan David Gonzalez-Ruiz

    (Departamento de Economía, Grupo de Investigación en Finanzas y Sostenibilidad, Universidad Nacional de Colombia, Medellín 050034, Colombia)

  • Mario Gongora

    (Research in Societal Enhancement (RISE), Institute for Artificial Intelligence (IAI), De Montfort University, Leicester LE1 9BH, UK)

Abstract

Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards.

Suggested Citation

  • Alejandro Pena & Juan C. Tejada & Juan David Gonzalez-Ruiz & Mario Gongora, 2022. "Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach," Sustainability, MDPI, vol. 14(11), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6668-:d:827556
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    References listed on IDEAS

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    1. Cosimo Magazzino & Marco Mele & Fabio Gaetano Santeramo, 2021. "Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
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    3. Shan-e-Ahmed Raza & Gillian Prince & John P Clarkson & Nasir M Rajpoot, 2015. "Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    4. Castanheira, Érica Geraldes & Acevedo, Helmer & Freire, Fausto, 2014. "Greenhouse gas intensity of palm oil produced in Colombia addressing alternative land use change and fertilization scenarios," Applied Energy, Elsevier, vol. 114(C), pages 958-967.
    5. Guifang Liu & Huaiqian Bao & Baokun Han, 2018. "A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, July.
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    Cited by:

    1. Manal Hamam & Daniela Spina & Roberta Selvaggi & Gabriella Vindigni & Gioacchino Pappalardo & Mario D'Amico & Gaetano Chinnici, 2023. "Financial sustainability in agri-food supply chains: A system approach," Economia agro-alimentare, FrancoAngeli Editore, vol. 25(2), pages 135-154.
    2. Hamam, Manal & Spina, Daniela & Selvaggi, Roberta & Vindigni, Gabriella & Pappalardo, Gioacchino & D’Amico, Mario & Chinnici, Gaetano, 2023. "Financial sustainability in agri-food supply chains: A system approach," Economia agro-alimentare / Food Economy, Italian Society of Agri-food Economics/Società Italiana di Economia Agro-Alimentare (SIEA), vol. 25(2), October.

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