Topdressing is essential for optimizing winter wheat yields. Compared to traditional ground tractors, Unmanned Aerial Vehicles (UAVs) offer distinct advantages for precise fertilizer application, including improved timeliness under wet soil conditions or in inaccessible areas, as well as the elimination of soil compaction and crop damage caused by tractor tracks. However, while the operational efficacy of UAVs is clearly established, the complex correlation between UAV operational characteristics and spatial distribution consistency remains to be fully elucidated. Hence, this study investigates the problems associated with the fertilizer use efficiency of UAV-applied topdressing for winter wheat. We calibrate UAV spreader systems using regression modeling, optimize parameters under various operational conditions, and compare fertilizer use efficiency in the field to improve application rate precision, granular fertilizer compatibility, and operational parameters for UAV spreading uniformity. The conventional spreader and the UAV were tested for fertilizer use efficiency based on spreading uniformity, NDVI index, chlorophyll content, dry matter yield, and theoretical wheat yield. The UAV has optimal spreading uniformity with a minimal coefficient of variation (CV) of 16.59
This study aimed to develop a tool capable of capturing the spatial variability of grapevine vigour from a geometric approach. The methodology was evaluated during the 2023, 2024, and 2025 growing seasons in a 1.27 ha trellised vineyard in Tarazona de la Mancha (Albacete, Spain). Green canopy cover (GCC) estimates were compared with reference data obtained using GEO-LAIC software across 33 uncrewed aerial vehicle (UAV) flights. Biomass volume (BV) estimates were compared with values derived from the alphashape algorithm using 10540 observations. The relationship between BV and pruning weight was also assessed to identify the optimal period for biomass estimation. GCC estimates showed strong agreement with GEO-LAIC, achieving coefficients of determination (R2) of up to 0.97 and root mean square error (RMSE) values below 0.04. BV estimates showed moderate agreement with the alphashape-derived values (R2 = 0.65), providing a basis for further calibration. The optimal period for estimating pruning weight was identified between BBCH growth stages 75 and 81. BV showed a stronger relationship with pruning weight than GCC. The proposed geometric approach effectively captures grapevine vigour variability while prioritising geometric accuracy over radiometric quality, potentially enabling the use of low-cost RGB sensors. This methodology offers a promising tool for precision viticulture and spatially targeted vineyard management.
Conventional visual evaluation methods often fail to detect herbicide-induced physiological responses during the initial post-application period, limiting the early assessment of herbicide efficacy for precision weed management. Therefore, this study investigated the potential of vegetation index-based hyperspectral phenotyping, combined with multivariate and machine-learning approaches, for the rapid diagnosis of the effectiveness of sequential herbicide programs in a maize-weed system. A controlled pot experiment was conducted using four sequential herbicide programs comprising pyroxasulfone fb halosulfuron methyl, pyroxasulfone fb tembotrione, atrazine fb halosulfuron methyl and atrazine fb tembotrione, along with an untreated weedy control. Visible-near infrared (VNIR; 400–1000 nm) hyperspectral imagery was acquired before post emergence herbicide application and at 2, 4, 6, and 9 days after application (DAA). Multiple vegetation indices representing chlorophyll status, canopy structure, pigment composition, stress responses, and water status were derived from hyperspectral reflectance data. Principal component analysis (PCA), correlation analysis, hierarchical clustering, and supervised classification models (logistic regression, random forest, and support vector machine) were employed to evaluate treatment discrimination and early diagnostic separability. Tembotrione-based treatments, particularly pyroxasulfone (PE) fb tembotrione (PoE), produced the strongest physiological responses, causing pronounced reductions in chlorophyll-, red-edge-, structural-, stress-, and water-related vegetation indices within four days after application. In contrast, halosulfuron methyl-based treatments exhibited spectral trends similar to the untreated control, indicating limited impact on the existing weed flora. Among the evaluated indices, GDVI, GNDVI, NDRE, mNDVI705, and mRESR were the most sensitive indicators of early herbicide stress. PCA revealed that the first two principal components explained more than 90
Efficient nitrogen (N) management is crucial for maximizing growth, bulb yield, and nutrient use efficiency in high-value horticultural crops, such as Borettana onion (Allium cepa L.). However, excessive or poorly timed N applications can reduce fertilizer use efficiency and increase the risk of environmental losses. This study aimed to develop and validate a growth-stage adaptive, N variable-rate application (N-VRA) strategy for Borettana onion under open-field conditions. The approach combined biomass estimation based on thermal time (growing degree days, GDD), determination of critical N requirements, and real-time crop monitoring through NDVI measurements. Four-year field trials conducted in northeastern Italy employed fixed-rate N applications (150–240 kg N ha⁻1) during 2021–2022 to develop the N-VRA algorithm, followed by 2023–2024 trials for testing and validation. Trials were arranged in randomized complete block designs with four to five replications per treatment on large plots (48 × 50 m to 48 × 74 m in size) Results showed that thermal time was able to accurately predict the growing biomass (R2 = 0.98; RMSE = 1.2 t ha⁻1); meanwhile, the critical N dilution curve effectively captured the relationship between biomass and optimal N concentration (R2 = 0.81; RMSE = 0.30
Herbicides and desiccants are critical for weed control and harvest management; however, their efficacy varies across crop species, environments, and growing seasons. This study evaluated unmanned aerial vehicle (UAV)-based multispectral imaging combined with machine-learning (ML) as a non-destructive approach for quantifying herbicide injury and desiccation responses across Canadian Prairie agroecosystems. Field trials were conducted from 2022 to 2024 at three sites in Alberta and Saskatchewanusing tame oat, oriental mustard, and lentil. Tame oat and oriental mustard were used as pseudo-weed models for wild oat and wild mustard, respectively. UAV multispectral imagery and concurrent ground-based visual ratings were collected from 0 to 21 days aftertreatment to characterize temporal crop responses. Six ML models were evaluated using UAV-derived vegetation indices. Ensemble models, including Gradient Boosting, Extreme Gradient Boosting, and Random Forest, consistently demonstrated strong predictive performance, with site-specific coefficients of determination (R) of 66–90
