Agriculture plays a central role in the global economy, offering vital income generation and employment opportunities (Phasinam et al., 2022). It holds critical responsibilities in ensuring food quality and safety, preserving the environment, fostering integrated rural development, and maintaining social structure and cohesion in rural areas (Loizou et al., 2019). For instance, in 2022, the European Union’s agricultural sector played a crucial economic role, contributing significantly with a gross value added of 222.3 billion euros. This amount represented about 1.4% of the total gross domestic product (GDP) of Europe. Particularly noteworthy was the relative increase in the estimated agricultural income per annual work unit, reaching a level 44.3% higher than that observed in 2015 (Eurostat, 2023). Furthermore, agriculture remained a crucial employer, with a staggering 8.7 million individuals employed in the agricultural sector across Europe in 2020, affirming its continued prominence within the EU (Eurostat, 2020). These data are projected to further surge in response to the expected increase in the global population, reaching 9.7 billion by 2050 (Pew Research Center, 2019). As evident from the data, the most substantial population increase is expected in Africa, with a projected boost of approximately 92.3% (Pew Research Center, 2019). Following by Latin America and Asia, which are expected to experience population growth by about 21% and 15.23%, respectively (Pew Research Center, 2019). The surge in population in specific regions has led to a notable escalation in food demand. A significant publication by Alexandratos and Bruinsma (2012) underscores the imperative need to increase global agricultural production by 60% to meet this growing food requirement. Developing countries are faced with an even greater challenge, as they would need to enhance agricultural output by 77%, while developed countries should aim for a 24% increase (Malhi et al., 2021). Consequently, the environmental impact of the agricultural sector has amplified, and in the next four decades, the emissions will increase by more than 60% (Fróna et al., 2019). In general, agriculture accounts for more than 11% of the total anthropogenic emission from direct source (Maraseni and Qu, 2016), and this value grows about 3-6% if the storage, transportation, packaging and agricultural input production are included (Tan et al., 2022). Considering direct agricultural emissions, 81% of the global ammonia (NH ₃) is reached by the agronomic sector (Damme et al., 2021) as a result of the increase in animal feeding operation (Schultz et al., 2019). NH ₃ has a high impact on the ecosystem leading to the acidification and eutrophication phenomena and also has a key role in the Particulate Matter 2.5 micrometers (PM ₂.₅) generation which is responsible for serious health problems such as chronic obstructive pulmonary disorder and lung cancer (Lelieveld et al., 2015; Apte et al., 2018). Other emissions from the agricultural sector are methane (CH ₄) and nitrous oxide (N ₂ O) which are greenhouse gases (GHGs) and contribute to climate change. They are produced during the enteric fermentation, manure management, synthetic fertilizer, manure management, synthetic fertilizers, rice cultivation, manure applied to soils and pastures, crop residues, cultivation of organic soils, and burning of crop residues (Han et al., 2019). So it is undeniable that agriculture has a very large influence on climate change, which also has a negative effect on agriculture itself. Indeed, agriculture, being highly susceptible to climate variations, experiences adverse consequences due to significant fluctuations in temperature and rainfall. These variations directly influence crop yields and quality, posing challenges to food production and agricultural sustainability. For instance, extended precipitations could delay production processes due to muddy soils and inaccessible fields for machinery, high temperatures cause the lack of winter chill induces a negative effect on the quality of asparagus and rhubarb and affect flowering time, the increase of CO ₂ induce the reduction of micro and macronutrients in lettuce, celery (Bisbis et al., 2018).

In order to mitigate the impact of climate change on agriculture and simultaneously reduce agriculture’s contribution to climate change embracing new technologies based on Data Science is required. In fact, data-driven decision-making holds the potential to revolutionize farming practices by enabling more efficient utilization of water, pesticides, and fertilizers, thereby minimizing environmental impacts (Akkem et al., 2023).

Nowadays, there are many new technologies based on the Internet of Things (IoT), wireless connection, cloud computing, and block-chain technology that have the potential to revolutionize crop monitoring. An example, is remote sensing technologies, such as satellite-based (Sentinel-3) or Unmanned Aerial Vehicle (UAV) systems, utilize spectral images to calculate reflected radiation (Toth and Jóźków, 2016). These images, when subjected to data analysis, provide valuable vegetation indices, including the widely used Normalized Difference Vegetation Index (NDVI) (Skakun et al., 2018), which assesses crop health based on the Red and Near Infrared reflectance. Beyond general vegetation indices, specific pigment content can be evaluated using remote sensing data. For instance, the Normalized Red Index quantifies chlorophyll levels, while the Normalized Green Index focuses on other pigments, excluding chlorophyll (Qi et al., 1994). In addition to remote sensing, field wireless sensor networks are employed to measure vital weather variables, such as temperature, air humidity, soil moisture, pH and so on (Priya and Yuvaraj, 2019). All these technologies guide agriculture toward a digital revolution, leading to the rise of precision agriculture (PA), which tackles the customization of agricultural practices to fit the unique characteristics of each crop, field, and environmental context. It advocates the adoption of cutting-edge technologies and data-driven approaches to effectively address the inherent heterogeneities within a field (Finger et al., 2019), providing an increase in terms of productivity using less natural resources such as energy and water (Pathan et al., 2020). PA finds broad applicability across various agricultural practices, offering valuable benefits in terms of resource efficiency and enhanced crop management. For instance, in the context of irrigation, PA enables precise water delivery, avoiding wastage and ensuring optimal water utilization. Similarly, in fertilization, PA plays a crucial role in identifying specific areas within the field where nutrients are needed, thereby providing targeted support to plant growth and minimizing resource losses due to over-application. Furthermore, PA’s impact extends to pest control and disease detection, where early warnings through predictive models enable proactive intervention, reducing potential damage and optimizing treatment strategies (Shafi et al., 2019). In Figure 1 are reported the domains where PA techniques are applied.

As evident from the data, the majority of publications in precision agriculture are concentrated in the crop domains (green). Specifically, disease detection (22%) and yield prediction (20%) stand out as the dominant subsections in research. The third most studied domain is livestock production, accounting for 12% of the publications. These new technologies are available in agriculture, paving the way for big data, and making it attractive for advanced data analysis methodologies such as Deep learning (DL) and Machine learning (ML), making them the most used in the recent literature for PA applications (Ayoub Shaikh et al., 2022). Here below are reported recent literatures about ML and DL techniques regarding Yield prediction and Disease detection, since these are the domains in which precision agriculture is most studied, then, another common class of model in PA applications is reviewed.

The objective of this concise review is to offer a comprehensive overview of the prevailing data science methodologies, highlighting their popularity and significance in the field. Indeed, an extensive portion of the literature is focused on machine learning and deep learning because the black-box/opaque AI methodologies may require less work from experts, albeit at the price of much more computational work because of the big sample size required. On the other hand, mechanistic-deterministic models take the other part of the literature with many applications ranging from fertilization management to disease predictions, but they often neglect the inferential uncertainty, with the risk of falsely over-accurate inferential statements. The MDMs clearly offer significant advantages in agrosystem management, enabling predictions across various scenarios of interest. To achieve this predictive power, a crucial step often involves calibration, which entails identifying optimal, context-specific parameter values (input values) for solving the underlying equations. These parameter values might be initially unknown, necessitating a comparison of observed data with predictions generated by the MDM. This process serves to assess the accuracy of the input values and is called trial-and-error procedure. Conversely, if the input values are sourced from literature or established knowledge, they are considered tuning parameters. However, regardless of the approach taken, both methods fail to quantify the forecast uncertainty inherent in the model (Kennedy and O’Hagan, 2001). In crop modelling with MDMs, the trial-and-error procedure is the most used (Della Nave et al., 2022; Rai et al., 2022; Terán-Chaves et al., 2022; Alvar-Beltrán et al., 2023) where the authors use historical data or build new experiments to achieve their prediction goals. Statistical procedures can be employed in the input value selection phase to facilitate uncertainty quantification in predictions. However, their application within these studies remains circumscribed, in part due to the involved nature of these techniques, but also for the prominent role played by the adopted calibration method on the resulting prediction errors (Gao et al., 2020).

The literature cited in this work highlights the limited number of contributions dealing with statistical methodologies in the PA field, particularly for crop yield prediction and disease detection; future work might consider the quantitative integration of the expert’s degree of belief into the decision-making processes of agriculture (Valleggi et al., 2023, 2024). This starting step also seems helpful in fully harnessing the power of modern structural causal models (Pearl, 2009) and improving decision-making in PA (Stefanini and Valleggi, 2022).

LV: Conceptualization, Writing – original draft. FS: Conceptualization, Writing – review & editing.