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

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.

Board gender diversity and cost of debt financing: Evidence from Latin American and the Caribbean firms

“…Regarding the model's overall performance, it achieved results similar to those reported by Peña et al. (2022) in configuring neural models with Deep learning structure and incorporating logistic activation functions. The above clearly evidences the suitable behavior exhibited by the model in the adaptation and learning stage concerning the characterization of the cost of debt for 2021.…”

“…The dimensional stability indices (FB, WSI, UAPC2, MRE) that measure the sensitivity of the model to the magnitude of the data characterizing the WACC-ST and WACC-LT distributions, it can be observed in Table 2 that the proposed model achieved stability indices that were below the stability threshold for the performance of a model by adaptation and learning (Definition 3), which is in agreement with the performance values established by the fuzzy evaluation model proposed by Park and Seok (2007) for these metrics. Regarding the model's overall performance, it achieved results similar to those reported by Peña et al (2022) in configuring neural models with Deep learning structure and incorporating logistic activation functions. The above clearly evidences the suitable behavior exhibited by the model in the adaptation and learning stage concerning the characterization of the cost of debt for 2021.…”

A Systematic Literature Review: Internet of Things on Smart Greenhouse

“…To improve the sustainability of agricultural cultivation, research and development methods are used integrated with deep learning, maps of planting sustainability forecasts can be obtained in real time using IoT networks [27].…”

Characterization of variables for modeling agroclimatic and phytosanitary events in agricultural crops using deep learning models

“…To ensure crop yield, it is important to prevent the occurrence of agroclimatic and phytosanitary events during the management of agricultural crops. These events can lead to affect crop production, affect the marketing of the harvested product, impose restrictions and quarantines on crops and productive zones, and affect the technical-financial analysis of an agricultural crop [4,5]. The main consequence of the last is that insurance and financial products to support the productive unit are very costly and unattractive for the agricultural sector, due to the high operational risk [4].…”

“…These events can lead to affect crop production, affect the marketing of the harvested product, impose restrictions and quarantines on crops and productive zones, and affect the technical-financial analysis of an agricultural crop [4,5]. The main consequence of the last is that insurance and financial products to support the productive unit are very costly and unattractive for the agricultural sector, due to the high operational risk [4]. For this reason, considering that soil variability from one area to another, climate variability and the type of plant grown have an impact on crop yields, it is necessary for farmers to combine these variables and use them to obtain a solution from crop management [3].…”