Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management that utilizes advanced technologies, such as sensor data analysis and automation, to optimize crop productivity, enhance farm income, and minimize environmental impacts. DPA encompasses various agricultural domains, including pest control, pest management, fertilization, irrigation management, sowing, transplanting, crop health monitoring, yield forecasting, harvesting, and post-harvest stages. Among the enabling technologies for DPA, Unmanned Aerial Vehicles (UAVs) have gained significant attention and market growth. The advancements in control systems, robotics, electronics, and artificial intelligence have led to the development of sophisticated agricultural drones. UAVs offer advantages such as versatility, quick and accurate remote sensing capabilities, and high-quality imaging at affordable prices. Furthermore, the miniaturization of sensors and advancements in nanotechnology enable UAVs to perform multiple operations simultaneously without compromising flight autonomy. However, various variables, including aircraft mass, payload capacity, size, battery characteristics, flight autonomy, cost, and environmental conditions, impact the performance and applicability of UAV systems in agriculture. The economic considerations involve the purchase of drones, equipment, and the expertise of trained pilots for flight management and data processing. Payload capacity, flight range, and financial factors influence the choice and implementation of UAVs in agriculture. The research and patent trends show the growing interest in UAVs for agricultural applications. This paper provides a general review of UAV types, construction architectures, and their diverse applications in agriculture until 2022.

In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis.

The definition of High Nature Value Farmland Areas (HNVF) was provided by Andersen in 2003: “HNVF comprises those areas in Europe where agriculture is the major (usually the dominant) land use and where that agriculture supports or is associated with either a high species and habitat diversity, or the presence of species of European conservation concern or both”. The present work focuses on an overview of the techniques used to produce HNVF maps at different spatio-temporal resolutions. The proposed approach is based on the statistical approach. The study area is the Basilicata region (southern Italy) in 2012, mapped at municipal spatial resolution. The HNVF areas were identified by applying a threshold to the sum of the contributions of the main characterizing indicators. Three indicators contribute to the calculation of the HNVF areas: crop variability (CD Index), extensive practices (EP Index), and the presence of natural elements (Index Ne). Good agreement was found between our HNVF map and the results of the literature, although the analysis approaches were different. The main advantages of the proposed methodology derive from only free input data being used, and include remote sensing images and the adaptability to different spatial resolutions (local, regional, and national).

The integration of machine learning (ML) into self-governing spraying systems is one of the major developments in digital precision agriculture that is significantly improving resource efficiency, sustainability, and production. This study looks at current advances in machine learning applications for automated spraying in agricultural mechanisation, emphasising the new innovations, difficulties, and prospects. This study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics, sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised, and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection, as well as accurate application of agricultural plant protection. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations’ efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advances, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods.

The integration of Machine Learning (ML) into autonomous spraying systems (1) is one of the major developments in digital precision agriculture (2) that is significantly improving resource efficiency, sustainability, and production. This study looks at current advancements in machine learning applications for automated spraying in agricultural mechanization (3), emphasising new innovations, difficulties, and prospects. The study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics (4), sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection (5) as well as accurate application of plant protection products. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations' efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advancements, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods.

The Senise red pepper, known as peperone crusco, is a protected geographical indication (PGI) product from Basilicata, Italy, traditionally consumed dried. Producers use semi-open greenhouses to meet PGI standards, but significant losses are caused by rot from microorganisms thriving in high moisture, temperature, and humidity, which also encourage pest infestations. To minimize losses, a low-cost alert system was developed. The study, conducted in summer 2022 and 2023, used external parameters from the ALSIA Senise weather station and internal sensors monitoring the air temperature and humidity inside the greenhouse. Since rot is complex and difficult to model, an artificial intelligence (AI)-based approach was adopted. A feed forward neural network (FFNN) estimated greenhouse climate conditions as if it were empty, comparing them with actual values when peppers were present. This revealed the most critical period was the first 3–4 days after introduction and identified a critical air relative humidity threshold. The system could also predict microclimatic parameters inside the greenhouse with red peppers, issuing warnings one hour before risk conditions arose. In 2023, it was tested by comparing predicted values with previously identified thresholds. When critical levels were exceeded, greenhouse operators were alerted to adjust conditions. In 2023, pepper rot decreased.