Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In this work, an operational methodology for characterization of vegetation biomass and nitrogen content based on close-range hyperspectral remote sensing is introduced. It is based on an unsupervised active learning technique suitable for the calibration of a partial least square regression. The proposed technique relies on an innovative usage of Shannon’s entropy and allows for the set-up of an incremental monitoring framework from scratch aiming at minimizing field sampling activities. Experimental results concerning the estimation of grassland biomass and nitrogen content returned RMSE values of 2.05 t/ha and 4.68 kg/ha, respectively. They are comparable with the literature, mostly relying on supervised frameworks and confirmed the suitability of the proposed methodology with operational environments.

Persistent environmental contaminants pose a substantial threat to agricultural ecosystems, necessitating robust methodologies for evaluation and mitigation of their effects. This study establishes a direct correlation between environmental impact assessment and sustainable agricultural management, showing the feasibility of using multi-scale bioindication and remote sensing technology to effectively monitor the impact of soil pollution in agricultural ecosystems. The key values of this research lie in the ability of the described approach to integrate advanced proximal/remote sensing and in situ analyses to assess the effects of soil contamination on bioindicators, providing a comprehensive framework for evaluating environmental stressors. The proposed methodology was tested on maize (Zea mays L.) and employs unmanned aerial vehicle-based multi/hyperspectral and thermal imaging to detect vegetation stress indicators such as normalized difference vegetation index and thermal anomalies. The interdisciplinary approach adopted in this research significantly enhances the value of the study by not only focusing on isolated results but also validating the entire methodological workflow. This cross-disciplinary integration ensures that the workflow retains its relevance across various environmental scenarios, enriching the results’ applicability and providing a robust framework for ongoing studies. The research objective of this work was achieved through experimental tests on soils contaminated with heavy metals and organic pollutants exceeding regulatory thresholds that revealed distinct spectral and thermal signatures, demonstrating the efficacy of integrated sensing for detailed environmental assessment. The findings underscore the role of bioindicators as pivotal tools for bridging environmental monitoring and sustainability by providing actionable insights into pollutant impacts and their cascading effects on ecosystems and human health. By equipping stakeholders with precise contamination detection tools, this study aims to provide a methodological approach to expand environmental impact assessment frameworks, supporting sustainable decision-making and risk management. These methodologies contribute to aligning agricultural practices with broader sustainability objectives, ensuring resilient food systems and ecosystem health.

Optimization of agricultural practices is crucial to ensure high and sustainable food production. In this regard, hyperspectral remote sensing demonstrated its usefulness in the building of models able to predict several vegetation parameters. The effort, in terms of time and costs, of a remote sensing campaign depends on both the instruments to be exploited and the number of samples to be acquired for model calibration. Therefore, the reduction of field activities is important to limit monitoring costs. The purpose of this work is to demonstrate that the combination of appropriate active learning strategies and regression models can dramatically reduce the field work with negligible degradation of model prediction performance. This has been assessed by processing two different datasets. In the first experiment, estimated ryegrass biomass and nitrogen content resulted in average RMSE values of 0.17 t/ha and 3.22 kg/ha against best literature values, on the same dataset, of 0.20 t/ha and 4.68 kg/ha. In the second test, the estimation of maize biomass and nitrogen content with a reduced calibration dataset provided, respectively, a RMSE of 0.65 t/ha and 11.1 kg/ha against a reference value of 7.1 kg/ha concerning the estimation of nitrogen content. Depending on the settings, the developed methodology provides results comparable with the literature or negligibly degraded, despite a reduction of the calibration dataset up to 80%.

Volume estimation of specific objects via close-range remote sensing is a complex task requiring expensive hardware and/or significant computational burden, often discouraging users potentially interested in the technology. This paper presents an innovative system for cost-effective near real-time volume estimation based on a custom platform equipped with depth and tracking cameras. Its performance has been tested in different application-oriented scenarios and compared against measurements and state-of-the-art photogrammetry. The comparison showed that the developed architecture is able to provide estimates fully comparable with the benchmark, resulting in a quick, reliable and cost-effective solution to the problem of volumetric estimates within the functioning range of the exploited sensors.

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (${\mathrm{R}}^2$ = 0.82; MAE = 6.36 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (${\mathrm{R}}^2$ = 0.86; MAE = 6.56 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons.