Nicola Silvestri

Site-specific weed management can reduce herbicide use and improve the sustainability of crop production, but its adoption remains constrained by the complexity and costs of many sensing technologies. Integrating weed detection algorithms into optical sensing systems already used for precision agriculture may represent a practical strategy to overcome these limitations. This study aimed to adapt the SWIM Weed Detection (SWIM-WD) module, originally developed for UAV imagery, to images acquired by the Augmenta Field Analyzer, a tractor-mounted optical sensor. The method was evaluated using images acquired in a commercial maize field. Calibration images acquired under weed-free conditions were used to derive crop structural parameters, whereas validation images containing weeds were used to evaluate two approaches, the Simple Method (SM) and an Improved Method (IM), the latter incorporating image-specific maize row detection. Both approaches provided Weed Green Cover (WGC) estimates showing high agreement with reference values (CCC = 0.87–0.92; R² = 0.82–0.89). The findings provide preliminary evidence that SWIM-WD can be adapted to a tractor-mounted sensing platform, supporting precision agriculture systems with weed detection capabilities without dedicated hardware. Further validation across fields, crop stages, and environmental conditions is required to assess the robustness and general applicability of the method.

The invasive aquatic plant Myriophyllum aquaticum represents both an ecological threat and a wet biomass disposal challenge. This study investigates hydrothermal carbonization (HTC) as a strategy for its valorisation into energy-dense hydrochar. A Design of Experiments–Response Surface Methodology (DoE-RSM) approach was applied to elucidate the combined influence of temperature (200–260 °C), residence time (30–210 min), and solid load (5–25 wt%) on hydrochar yield and properties. Hydrochar yields ranged from 48.8% to 65.6%, with the highest yields achieved at 200 °C, 30 min, and 25 wt% solids. Higher heating values of hydrochars spanned from 12.14 to 14.53 MJ/kg, corresponding up to +19% energy densification at higher process severity. Carbon and energy yields reached 69.7% and 68.6%, respectively, with maximum values attained under low-severity, high-solid-load conditions. The predictive models exhibited strong agreement with experimental data, enabling optimisation of HTC parameters for targeted hydrochar applications. Two hydrochars, “peat-like” and “lignite-like”, were further characterised for their potential use as soil amendments. The lignite-like hydrochar complied with EU contaminant limits and showed no phytotoxicity, confirming its suitability for agronomic use. Overall, HTC of M. aquaticum provides an effective waste-to-resource pathway, transforming wet invasive biomass into value-added carbon materials.

Agroforestry, an integrated land-use practice combining trees and woody shrubs with crop and animal farming, offers significant ecological and agricultural benefits, including enhanced biodiversity, improved soil fertility, and increased resilience to environmental pressures. Despite its advantages, agroforestry faces challenges such as high initial investments, long maturation periods for trees, land tenure issues and a high level of complexity in technical management. Digital agriculture introduces advanced technologies and sensors, which provide precise data on soil moisture, nutrient levels, and plant health, enabling more efficient resource use and better farm management. Integrating these sensing technologies into agroforestry can address key challenges, optimize irrigation and nutrient management, and enhance overall system productivity and sustainability. This review explores the interaction between agroforestry and digitalization, highlighting case studies, and discusses the potential for these technologies to support sustainable agriculture and climate change mitigation. Increased investment in research and development, along with supportive policies, is essential for advancing the adoption of these innovative practices in agroforestry.

Agroforestry systems offer clear environmental and agronomic advantages, but their effect on plant–biotic stressor interactions remains poorly understood. Specifically, the shade from companion trees can create microclimates favorable to fungal diseases on herbaceous crops. This potential drawback may offset other benefits, highlighting the urgent need for advanced plant health monitoring in these systems. This study assessed the potential of hyperspectral reflectance to detect the single and combined effects of simulated tree shading and infection by the fungal pathogen Alternaria alternata on grain sorghum (Sorghum bicolor L. Moench) under rainfed field conditions. Sorghum was grown either under full light or 50% shading conditions. Half of the plots were artificially inoculated with an A. alternata spore suspension (2 × 10⁸ CFU mL⁻¹), while the others served as controls. Leaf and ground-canopy measurements were acquired with a full range spectroradiometer (VNIR-SWIR, 400–2,400 nm) and UAV imagery covered the VIS-NIR range (400–1,000 nm) before the onset of visible symptoms. Permutational multivariate analysis of variance of leaf and ground-canopy data revealed significant effects of shading (Sh), infection (Aa), and their interaction (p < 0.05), allowing early detection of infection two days before symptom appearance, while UAV data showed only singular significant effects. Partial least squares discriminant analysis accuracy reached 78% at the leaf level, 90% at the ground-canopy level, and 74% (Sh) and 75% (Aa) at the UAV scale. Furthermore, vegetation spectral indices derived from the spectra confirmed greater physiological stress in shaded and infected plants, consistent with disease incidence assessments. Our results establish scale-specific hyperspectral reflectance spectroscopy as a powerful, non-destructive technique for early plant health surveillance in agroforestry. This advanced optical sensing capability is poised to illuminate complex stressor interactions, marking a significant step forward for precision agroforestry management.

The success of Variable Rate Application (VRA) techniques is closely linked to the algorithm used to calculate the different fertilizer rates. In this study, we proposed an algorithm based on the integration between some estimated agronomic inputs and crop radiometric data acquired by using a multispectral sensor. Generally, VRA algorithms are evaluated by comparing the yields, but they can often be affected by factors acting in the final phase of the crop cycle and not dependent on the fertilization treatments. Therefore, we decided to compare our algorithm (ALG) versus the traditional application of fertilizer (TRD) by evaluating the crop growth 1.5 months after the fertilization time. The algorithm was tested on a sorghum crop under organic farming, managed with or without manure. The saving of N obtained with ALG was equal to 14 and 5 kg ha^− 1 (-14 and − 10% for the non-manure and fertilized treatments, respectively). The NDVI values acquired after fertilization showed a remarkable reduction of relative standard deviation for ALG system (from 22 to 9% and from 34 to 14% for manured and not manured, respectively), which was not found for TRD system (from 16 to 17% and from 29 to 18% for manured and not manured, respectively). The above ground biomass produced was statistically equivalent for the two systems in the manured plots and significant higher for ALG in not-manured plots (+ 0.74 t ha^− 1 of dm, equal to + 23%). Finally, the indices calculated to evaluate the Nitrogen Use Efficiency (NUE) were consistently better in the ALG theses.

In the present study, we implemented an unsupervised learning procedure, a self-organizing map (SOM), for characterizing the main agricultural land systems (ALS) in western Mediterranean areas. Input data derived from national agricultural censuses of two periods (2000 and 2010) at the municipality level. The SOM allowed us to aggregate the items into clusters based on the proximity between the associated input variables. The main clusters were then mapped back to the geographical space and interpreted in terms of ASL typologies. The main ALS from the census 2000 included one permanent grassland system with extensive farming; two arable land systems, corresponding to winter and summer crops; and two permanent cropland systems, relatable to intensively cultivated or marginal areas. The ALS from the census 2010 included only one arable land system with a non-intensive use of irrigation; two permanent cropland systems similar to those found in 2000; one more extensive permanent grassland system; and a mixed system characterized by permanent grassland and arable land. In summary, the main trends emerging from the transitions between the two censuses periods were: i) a reduction in agricultural land use; ii) an increase in utilized agricultural and irrigated area; iii) a contraction in arable land and permanent grassland. Using a data-driven approach such as SOM allowed us to discover hidden patterns in the input census data. Therefore, the prevalent agricultural typologies characterising the ALS in the two analysed periods resulted to be shaped by the reality of the surveyed area solely, with regard to its agronomic assessment.

Knowledge about the spectral response of camelina under different regimes of nitrogen (N) fertilization is very scarce. Therefore, 2-year open-field trials were carried out in the 2021 and 2022 growing seasons with the aim of evaluating the spectral response of spring camelina to four different N fertilization regimes by using remote (UAV) and proximal (leaf-clip Dualex) sensing techniques. The tested treatments were: (i) control: no N application (T0); (ii) top dressing: 60 kg N ha⁻¹ before stem elongation (T1); basal dressing: 60 kg N ha⁻¹ at sowing (T2); basal + top dressing combination: 60 kg N ha⁻¹ at sowing + 60 kg N ha⁻¹ before stem elongation (T3). Camelina seed yield and N use efficiency were strongly affected by fertilization regimes, with the best results obtained at T2. A reduction in plant development and seed yield was detected in 2022, probably due to the rise in air temperatures. A significant effect of both growing season and N fertilization was observed on the photosynthetic pigments content with the T1 highest values in 2022. The highest seed oil content was achieved at T1, while the protein content increased with increasing N, with the best values at T3. Positive and significant correlations were observed among several vegetation indices obtained through UAV flights (NDVI, MRS705, FGCC) and seed yield, as well as between FGCC and leaf N concentration. Overall, these findings demonstrate the feasibility of utilizing remote sensing techniques from UAVs for predicting seed yield in camelina.

The estimation of the infestation level in a field and the consequent determination of the economic threshold is a basic requisite to rationalize post-emergence weeding. In this study, a simple and inexpensive procedure to determine the economic threshold based on weed cover is proposed. By using high-resolution RGB images captured by a low-cost drone, a free downloadable app for image processing and common spreadsheet software to perform the model parametrization, two different methods have been tested. The first method was based on the joint estimation of the two parameters involved in weed cover calculation, whereas the second method required the availability of further images for the separate estimation of the first parameter. The reliability of the two methods has been evaluated through the comparison with observed data and the goodness of fit in parameter calibration has been verified by calculating appropriate quality indices. The results showed an acceptable estimation of the weed cover value for the second method with respect to observed data (0.24 vs. 0.17 m² and 0.17 vs. 0.14 m², by processing images captured at 10 and 20 m, respectively), whereas the estimations obtained with the first method were disappointing (0.35 vs. 0.17 m² and 0.33 vs. 0.14 m², by processing images captured at 10 and 20 m, respectively).

Nature-Based Solutions (NBSs) are considered worldwide as a suitable approach for mitigating the impact of industrial agriculture on sediments and nutrient losses. However, their actual effectiveness is still questioned. In cases where site measurements of NBS performance are scarce, models can provide useful insights if accurately set. This study analyzed the effects of vegetated buffer strips (VBSs) and winter cover crops (WCCs) planted in some herbaceous cropping systems within the Massaciuccoli reclamation area (Vecchiano, Central Italy). Analyses stem from modelling water and soil dynamics by applying SWAT+ at field scale on high resolution close-range photogrammetric digital terrain model (DTM), real crop rotations, and a detailed calendar of the main agronomic interventions. The NBS implementation was modelled in two experimental areas, showing contrasting soil properties. Comparing results from the modelling of different scenarios highlighted that NBS mitigative effect is influenced by soil properties and local topographic irregularities, which could induce concentrated flows. Long term climate changes can induce relevant different effects by varying the nature of soil.