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.

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).

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 Apuan Alps are a mountain range in the north-west of Tuscany, part of the Tuscan Sub-Apennines. The particular geomorphology, exposure, and biogeographic location create a diversified vegetation landscape across altitudinal and latitudinal gradients. The sclerophyllous formations of the Mediterranean scrub and woods of the maritime side coexist with mesophilous broadleaf woods and grasslands at higher elevations. In this context, it is possible to find heterotopic patches of evergreen sclerophyllous vegetation in the inner mountain complex, closely linked to the external walls of limestone reliefs. These sites, often characterized by superficial and deep karst phenomena, reveal the presence of underground cavities frequented by humans since the post-glacial period. This article presents a case study involving field surveys and UAV (Unmanned Aerial Vehicle) technology in a limestone cave near Tenerano (Massa-Carrara, Tuscany), to identify the species and the plant communities of the site for a historical reconstruction of the natural plant landscape.