Antonio Persichetti

Hyperspectral imaging adoption is hindered by high investment costs, and multicollinearity among contiguous bands requires dimensionality-reduction (DR) algorithms, raising concerns about cost-effectiveness for farmers. In this study, we propose a direct comparison between feature extraction and feature selection methods as DR strategies for monitoring the nitrogen (N) status of processing tomato. The hyperspectral reflectance (399–1001 nm) was monitored in a processing tomato experiment with four N levels alongside crop traits (aboveground biomass, AGB; N uptake; N nutrition index, NNI). Gaussian process regression (GPR) was trained in two DR scenarios, (i) feature extraction using principal component analysis (GPR-PCA) and (ii) feature selection, with a sequential backward band removal (band analysis tool, BAT, in ARTMO software (v3.30); GPR-BAT), to identify the most informative key bands. The GPR-PCA achieved the highest mean accuracy for AGB, whereas for N uptake and NNI GPR-BAT yielded comparable estimation performance. Selected bands were located in the NIR for all variables, in the red-edge for N uptake and NNI, and in the blue region for NNI, and were interpreted as candidate spectral regions. Based on the identified bands, two candidate multispectral sensor configurations were proposed for N monitoring: an AGB–NNI scenario (6 bands) and an AGB–N uptake scenario (5 bands). These findings suggest the potential of task-specific multispectral sensors for crop N monitoring, although NNI accuracy (RMSE ≈ 0.11 NNI units) remains insufficient for decisions close to the fertilization threshold.

Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands.