As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.

Studying mango (Mangifera indica L.) fruit development represents one of the most important aspects for the precise orchard management under non-native environmental conditions. In this work, precision fruit gauges were used to investigate important eco-physiological aspects of fruit growth in two mango cultivars, Keitt (late ripening) and Tommy Atkins (early-mid ripening). Fruit absolute growth rate (AGR, mm day⁻¹), daily diameter fluctuation (ΔD, mm), and a development index given by their ratio (AGR/ΔD) were monitored to identify the prevalent mechanism (cell division, cell expansion, ripening) involved in fruit development in three (‘Tommy Atkins’) or four (‘Keitt’) different periods during growth. In ‘Keitt’, cell division prevailed over cell expansion from 58 to 64 days after full bloom (DAFB), while the opposite occurred from 74 to 85 DAFB. Starting at 100 DAFB, internal changes prevailed over fruit growth, indicating the beginning of the ripening stage. In Tommy Atkins (an early ripening cultivar), no significant differences in AGR/ΔD was found among monitoring periods, indicating that both cell division and expansion coexisted at gradually decreasing rates until fruit harvest. To evaluate the effect of microclimate on fruit growth the relationship between vapor pressure deficit (VPD) and ΔD was also studied. In ‘Keitt’, VPD was the main driving force determining fruit diameter fluctuations. In ‘Tommy Atkins’, the lack of relationship between VPD and ΔD suggest a hydric isolation of the fruit due to the disruption of xylem and stomatal flows starting at 65 DAFB. Further studies are needed to confirm this hypothesis.

Soil and water pollution caused by excessive use of fertilizers and resource scarcity are critical issues in modern horticulture. Although laboratory tests are reliable, they take time and use chemical reagents that must be disposed of and complex protocols. Monitoring plant nutrient status through technologies that allow continuous and rapid assessment is crucial for precise resource management. Several proximal and remote sensors that use different physico-chemical principles to monitor plant nutrient status are available nowadays. However, these technologies still have important operative and structural limitations that must be overcome. The aim of this review is to summarize the current status and latest developments in proximal and remote sensors capable of monitoring plant and soil nutrients, focusing on sensor types, principles, applications, and their strengths and weaknesses. Electrochemical proximal sensors allow continuous monitoring of nutrients in the plant sap or in the soil solution but work on a single spot basis. Instruments based on optical sensors allow immediate measurements and quick analysis, but do not work on a continuous basis. On the other hand, remote sensors, such as drone-mounted cameras and satellite systems, are based on large-area imaging and can be used to estimate crop nutrient status by processing images at different wavelengths. Finally, combining proximal and remote techniques may be needed to achieve very accurate monitoring of plant and nutrient status.

Two different olive cultivars grown under a high-density hedgerow system were studied to compare their fruiting and branch architecture features and to determine the possibility to use ‘Calatina’ olive trees for intensive plantings, as a local alternative to the international reference ‘Arbequina’. Weights of two-year-old branches, fruits and leaves were recorded to estimate the growth partitioning. Growth and architectural parameters, such as shoot length, vector and diameter, branching angle, branch total length, height, width, area, and branching frequency, were determined by digital image analysis. Digital images of the fruits were also used to estimate fruit maturation by peel color analysis. Whole branch and fruit crop weights were similar in the ‘Arbequina’ and ‘Calatina’, while the latter had a greater fruit/leaf ratio, showing a higher production efficiency than ‘Arbequina’. Fruits were fewer but bigger in ‘Calatina’ than in ‘Arbequina’, suggesting an advantage for both trunk-shaking and straddle machine harvesting in the Sicilian cultivar. Leaf/wood ratio, branching frequency and branching angle were similar in the two cultivars. ‘Calatina’ shoots exhibited a greater bending degree than those of ‘Arbequina’ and this trait particularly favors straddle harvesting. In addition to many similarities between the two cultivars, the present study indicates that ‘Calatina’ is more efficient in terms of yield and harvesting than ‘Arbequina’. This qualifies ‘Calatina’ as a superior, yield-efficient olive cultivar suitable for intensive hedgerow plantings to be harvested with straddle or side-by-side trunk shaker machines.

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of ‘Calatina’ olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψ_stem) and stomatal conductance (g_s) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψ_stem and g_s, while remote sensing data were correlated only with Ψ_stem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψ_stem and g_s. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψ_stem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψ_stem, achieving high R² values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.