Riccardo Lo Bianco

Efficient nutrient management is essential for sustainable orchard production; however, conventional laboratory analyses used to assess plant nutritional status are time-consuming and expensive. Optical sensing technologies offer a rapid and non-destructive alternative. This study evaluated the potential of proximal optical sensors and UAV-based multispectral imagery to assess the nutritional status of young potted ‘Nashi’ pear (Pyrus pyrifolia (Burm. f.) Nakai) trees. Three fertilization treatments based on different concentrations of Hoagland solution were applied to 18 one-year-old potted trees. Leaf measurements were collected during the growing season using Dualex, CL-01 chlorophyll meter, and Pocket PEA fluorimeter, while UAV-based multispectral imagery was used to calculate vegetation indices, including NDVI, SR, OSAVI, and MSAVI. Leaf nitrogen (N), phosphorus (P), and potassium (K) concentrations were chemically determined and used as reference values for the regression analyses. Significant (p < 0.05) relationships were observed between leaf N content (N%) and several optical parameters related to leaf pigments, including chlorophyll, flavonols, and the Nitrogen Balance Index (NBI), as well as multispectral indices, although with weak associations (R² = 0.326–0.488). The strongest individual relationship with N% was shown by NBI (R² = 0.480). To account for repeated measurements on the same plants, linear mixed-effects models were fitted. These models indicated that NBI showed the strongest association with N% among the proximal optical parameters (β = 0.019, p < 0.001; RMSE = 0.113; MAE = 0.091), followed by flavonols and Dualex chlorophyll. In contrast, optical parameters showed limited sensitivity to P and K. Multispectral indices were not significantly related to K, while only Red and Green reflectance showed weak correlations with P. Overall, optical parameters showed the best associations with N% under the combined nutrient-gradient conditions tested, whereas the assessment of P and K remained limited and should be considered exploratory.

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.

This study evaluated growth, yield and olive oil quality of mature pedestrian olive orchards. Trees of three Sicilian cultivars Calatina, Nocellara del Belice and Abunara were planted at four combinations of planting densities and training forms. Trees at 2 × 5 m were trained to central leader (CLx2), those at 3 × 5 m to free palmette (FPx3), those at 4 x 5 to globe vase (GVx4), and those at 5 x 5 to poly-conic vase (PVx5). ‘Calatina’ had the smallest trees in terms of trunk size in all growing systems, while canopy size of trees at higher densities was similar for all three cultivars. ‘Calatina’ was also the most growth efficient (m3 of canopy per cm2 of TCSA) and produced the least amount of pruning wood in the hedgerow systems (CLx2 and FPx3). Fruit yield per tree tended to be higher in more vigorous cultivars (Abunara and Nocellara) grown to 3D systems (GVx4 and PVx5), while ‘Calatina’ was the most yield efficient (kg of fruit per cm² of TCSA) especially in the hedgerow growing systems. Fruit and oil yield per ha and average production value tended to be highest in CLx2 trees and lowest in GVx4 trees, with ‘Calatina’ showing the sharpest changes and ‘Nocellara’ the smallest changes among growing systems. According to the Jaen index, CLx2 tended to induce earlier fruit maturation, followed by PVx5, GVx4, and FPx3. The growing system did not affect oil fatty acid composition, while ‘Calatina’ had the highest amount of mono-unsaturated fatty acids and the lowest amount of saturated fatty acids. ‘Abunara’ oils exhibited the highest amount of total phenols in CLx2, while ‘Calatina’ and ‘Nocellara’ oils exhibited the highest amount in FPx3 and PVx5. Both, trans-2-hexenal (“cut grass” sensory note) and hexenyl acetate (“floral” sensory note) tended to be lowest in oils from trees grown at CLx2 and highest in those from trees grown at GVx4, showing a somewhat inverse relationship with fruit ripening degree. The outcome of the present study on mature pedestrian orchards shows that proper combinations of cultivars, planting densities, and training forms (canopy shape) may result in efficient intensive systems for growing olive in areas where super-high density systems cannot be profitable due to agronomic and environmental limitations (water shortage, steep sloping sites, small farm size, etc.). Pedestrian growing systems can also be used to exploit olive biodiversity by allowing the use of available local genotypes. For this reason, they may represent an effective and sustainable solution against unexpected climate changes and associated emerging diseases.

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.

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Determining the influence of vapor pressure deficit (VPD) on fruit growth is a key issue under a changing climate scenario. Using a comparative approach across different fruit tree species may provide solid indications of common or contrasting plant responses to environmental factors. Knowing fruit growth responses to VPD may also be useful to optimize horticultural management practices under specific atmospheric conditions. Climate data to calculate VPD and fruit relative growth rates (RGR) by fruit gauges were monitored in peach at cell division, pit hardening and cell expansion stages; in two mango cultivars at cell division, cell expansion and maturation stages; in two olive cultivars, either full irrigated or rainfed, at early and late cell expansion stages; in ‘Valencia’ orange at early and late cell division stage, before and after mature fruit harvest; in loquat at cell expansion and maturation stages. At the fruit cell division stage, sensitivity of fruit growth to VPD seems to vary with species, time, and probably soil and atmospheric water deficit. ‘Keitt’ mango and ‘Valencia’ orange fruit growth responded to VPD in opposite ways, and this could be due to very different time of the year and VPD levels in the monitoring periods of the two species. At pit hardening stage of peach fruit growth, a relatively weak relationship was observed between VPD and RGR, and this is not surprising as fruit growth in size at this stage slows down significantly. A consistent and marked negative relationship between VPD and RGR was observed at cell expansion stage, when fruit growth is directly depending on water intake driving cell turgor. Another behavior common to all observed species was the gradual loss of relationship between VPD and RGR at the onset of fruit maturation, when fruit growth in size is generally programmed to stop. Finally, regardless of fruit type, VPD may have a significant effect on fruit growth and could be a useful parameter to be monitored for tree water management mainly when the cell expansion process prevails during fruit growth.

The aim of the Special Issue “Precision Management of Fruit Trees” was to collect new insights to support the adoption of advanced, efficient, and sustainable management techniques in the fruit production sector

Exploiting biodiversity must be considered today an effective strategy to improve the sustainability of olive production systems. The evaluation of local cultivars, based on their vegetative and fruiting traits, along with an analysis of product quality, may contribute significantly to the development and diffusion of new olive-growing systems. The aim of this study was to evaluate growth, productivity, and olive oil quality of three Sicilian cultivars with different vigor/growth habit grown in four different combinations of training form and planting density. ‘Abunara’, ‘Calatina’, and ‘Nocellara del Belice’ olive trees were planted in four different intensive planting systems: 2 × 5 m trained to central leader (CLx2), 3 × 5 m trained to free palmette (FPx3), 4 × 5 m trained to small globe vase (GVx4), and 5 × 5 m trained to poly-conic vase (PVx5) and evaluated for seven years. Planting systems with low-density showed faster growth (trunk cross section area and canopy volume) than high-density systems. High-density systems had higher yield per hectare but lower yield per tree than low-density systems. ‘Calatina’ was the least vigorous but most productive cultivar. ‘Abunara’ and ‘Nocellara’ were relatively vigorous and suffered the tight spacings of high-density systems. Yield efficiency was generally high in ‘Calatina’, and it showed an increase with time in ‘Abunara’ and ‘Nocellara’ grown under the GVx4 and PVx5 systems. Fruit yield per hectare was highest in ‘Calatina’ grown under high-density systems. Oil yield was lower in ‘Nocellara’ than in ‘Abunara’ and ‘Calatina’. In terms of oil quality, ‘Calatina’ and ‘Abunara’ produced oils with higher oleic acid content than ‘Nocellara’. Generally, ‘Calatina’ has production characteristics of considerable interest, which, combined with low canopy volume and vigor, make it suitable for intensive pedestrian olive orchards with high levels of harvest and pruning mechanization and using different strategies and machines. Overall, for their management flexibility, these planting systems can contribute to improve sustainability of the olive industry even in sloping, dry areas of the Mediterranean.