The adoption of Sustainable Development Agenda 2030 (SDA) by the United Nations in September 2015 led this organisation to raise several concerns about the effects of climate change on the planet. One sector that has generated significant attention for its greenhouse gas emissions (GHG) to the atmosphere has been the agricultural sector. For this reason, several governmental and non-governmental entities such as FAO (UN Food and Agricultural Organization) have begun to promote a series of initiatives to achieve a balance between agricultural development and sustainability in the context of the planet’s food security [1]. 

Operational risk (OR) is one of the critical concepts to achieve organizations’ environmental and financial sustainability. According to the Basel II agreement, operational risk (OR) is defined as “…the possibility of incurring in losses due to deficiencies, failures or inadequacies in human resources, processes, technologies, infrastructure or by the occurrence of external events…” ^[2][8]. OR has emerged as a key concept for characterising risks arising from phytosanitary and climatic events in agricultural crops and is described by aggregate loss distribution (ALD). 

Researchers have used different technologies to achieve crops’ financial and environmental sustainability. These include spectral and satellite images ^[3][16], and unmanned aerial vehicles (UAVs) for automatic fumigation and fertilization systems ^[4][5][17,18]. Additionally, IoT-IoB (Internet of Things and Beings) platforms for real-time monitoring have performed an essential role ^[6][19]. Some authors have applied machine-learning models (ML) combined with the techniques mentioned above to assess risk parameters in agricultural operations. To achieve a balance between sustainability and development in crops based on the SDA-2030, four development trends can be identified in the scientific literature ^[7][20]:

A first development trend focuses on the use of multi (MIs) and hyper-spectral (HIs) images for the non-destructive phytosanitary diagnosis of crops in situ ^[8][21]. A first group of papers shows how MAIs have helped detect phytosanitary events. As proof of this, ref. ^[9][22] identified the impact of Mildiu on leaves in tomato cultivation, and ref. ^[10][23] characterised the yellow striation on maize crops. Finally, ref. ^[11][24] determined the biochemical characteristics and physiological features of PEs in wheat crops. In this same group, ref. ^[12][25] showed how MAIs have been used to assess the productivity of macadamia trees. A second group of papers focuses on MAIs to improve risk management in oil-palm crops. In this way, ref. ^[13][26] described the relevance of MAIs and VIs for precision farming in oil-palm crops, ref. ^[14][27] described the use of advanced classifiers for the diagnosis of healthy oil-palm units from MAIs. Finally, ref. ^[15][28] showed a methodology for the use of MAIs to characterise PEs in different crops. This development trend shows how recent advances in optical remote sensing, including camera systems and spectral data analysis, allow the non-destructive diagnosis of phytosanitary events (PEs), improving the process of detecting diseases in crops. Although satellite images (Sis) are an excellent alternative for the monitoring and characterisation of PEs in oil-palm crops located over large areas of land, the frequency for capturing, the required resolution, and the associated costs for processing these images is a barrier to decision makers ^[16][29]. A second development trend focuses on designing vegetation indices (VIs) using multi-spectral images (MIs). The NDVI (Normalised difference vegetation index) is one of the most cited, e.g., in the area of evaluating the plant vigour in areas of considerable agricultural coverage ^[17][30]. This index is also used in combination with others, such as GNDVI (green normalised difference) and SAVI (soil adjusted), to determine plant vigour in vineyards and tomato crops ^[13][26]. While some authors have discussed the applicability of MIs for the diagnosis of vegetation states in different agriculture crops ^[9][22], others have developed VIs by using just MIs, obtaining satisfactory results. Some researchers have developed VIs based on multi-spectral aerial images (MAIs) taken with unmanned aerial vehicles (UAVs), e.g., for the spatial characterisation of oil-palm crops ^[14][27], and for the detection and diagnosis of phytosanitary states in different crops ^[15][28]. In addition, these MAIs have been used for the identification of fruits in coffee crops ^[10][23], for the treatment of weeds ^[18][31] and for the control of deforestation processes ^[19][32]. It is essential to highlight the preponderance achieved by the MAIs for diagnosing crop health, overcoming the limitations of SIs in monitoring units for different crops. It is also necessary to highlight the technological development of hyperspectral images (HIs); however, the creation of VIs for the diagnosis of PEs using this technology is still at a very early stage of development ^[5][18]. A third development trend focuses on creating augmented-intelligence platforms (AIPs) to improve the real-time characterisation of crops. These platforms aim to integrate different technologies for the diagnosis of PEs, among which RGB (red-green-blue) and MI images, and ML and DL models, stand out ^[20][21][22][33,34,35]. Other researchers have pushed these platforms by integrating IoT (Internet of Things) devices, such as optical and multi-spectral sensors and technologies for communication (LORA, Zigbee). In this way, ref. ^[23][36] mapped arctic vegetation, ref. ^[6][19] showed the ecological monitoring of open-space species, and ref. ^[24][37] improved the autonomy of unmanned aerial vehicles (UAVs), identifying disease hot spots located over large areas of crops, supported by ML and DL models. Within ML and DL modelling to support AIPs in monitoring crops, ref. ^[25][38] presented a convolutional neural model (CNN) to detect pine trees affected by wilt using MAIs, and ref. ^[26][39] presented a set of ML algorithms to improve irrigation process in vineyards also using MAIs. Finally, ref. ^[27][40] presented a preliminary analysis of pathology detection in oil-palm crops using convolutional neural networks (CNN) integrating MAIs and VIs. Finally, ref. ^[27][40] presents a preliminary analysis of pathology detection in oil-palm crops using convolutional neural networks (CNN) integrating MAIs and VIs. This development trend shows how AIPs have enabled efficient real-time crop management by integrating IOT technologies. However, it can be observed that there is an absence of AIPs that integrate models for adaptation and learning to identify the dispersion dynamics or for the characterisation of risks derived from a PE in crops. A fourth development trend focuses on the design of parametric insurances based on the characterisation of operational risk (OR) for different management scenarios ^[28][41]. Within this trend, the first group of studies shows how the ML concepts have been used to model OR ^[29][42]. Some researchers have defined fuzzy-inference models for the qualitative description of scenarios for OR management ^[2][8] or to determine the inherent risk as a result of implementing different management scenarios when mitigating OR ^[30][43]. In addition, the estimation of this risk through the integration of multi-dimensional databases is available in ^[31][9]. Within this trend, a second group of studies focuses on the configuration of parametric insurances in developing countries ^[32][44]. The first study presents a series of recommendations to achieve the sustainability of oil-palm crops through the characterisation and identification of operational and reputational risks ^[33][45]. In contrast, a second study shows the configuration of parametric insurances concerning risk related to changing weather conditions ^[34][46]. Furthermore, it has been demonstrated how insurance contracts can be designed based on a farmer satisfaction index by integrating statistical analysis of agro-climatic data and by applying optimisation techniques for improving the coverage for catastrophic risks ^[35][47]. Ref. ^[36][48] shows how neural networks have been used for credit-risk modelling by analysing the relationship between access to credit and productivity in the agricultural sector for a large set of countries. Due to the importance of OR in the design of insurance products for the protection of farming activities, in this development trend, as in the case of the previous trend, it is observed that there is an absence of models integrating ML and financial-risk concepts for the improvement of the environmental and financial sustainability of crops ^[7][20].