Automatic detection of foliar diseases in potato fields, such as early blight caused by Alternaria solani, could allow farmers to reduce the application of plant protection products while minimizing production losses. UAV-based, high resolution, NIR-sensitive cameras offer the advantage of a detailed top-down perspective, with high-contrast images ideally suited for detecting Alternaria solani lesions. A field experiment was conducted with 8 plots housing 256 infected plants which were monitored 6 times over a 16-day period with a UAV. A modified RGB camera, sensitive to NIR, was combined with a superzoom lens to obtain ultra-high-resolution images with a spatial resolution of 0.3 mm/px. More than 15,000 lesions were annotated with points in two full size images corresponding to 1250 cropped tiles of 256 by 256 pixels. A deep learning U-Net model was trained to predict the density of Alternaria solani lesions for every pixel. In this way, density maps were calculated to indicate disease hotspots as a guide for the farmer.

In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).

Leek farmers tend to apply too much nitrogen fertilizer as its cost is relatively low compared to the gross value of leek. Recently, several studies have shown that proximal sensing technologies could accurately monitor the crop nitrogen content and biomass. However, their implementation is impeded by practical limitations and the limited area they can cover. UAV-based monitoring might alleviate these issues. Studies on UAV-based vegetable crop monitoring are still limited. Because of the economic importance and environmental impact of leeks in Flanders, this study aimed to investigate the ability of UAV-based multispectral imaging to accurately monitor leek nitrogen uptake and dry biomass across multiple fields and seasons. Different modelling approaches were tested using twelve spectral VIs and the interquartile range of each of these VIs within the experimental plots as predictors. In a leave-one-flight out cross-validation (LOF-CV), leek dry biomass (DBM) was most accurately predicted using a lasso regression model (RMSE_ct = 6.60 g plant⁻¹, R²= 0.90). Leek N-uptake was predicted most accurately by a simple linear regression model based on the red wide dynamic range (RWDRVI) (RMSE_ct = 0.22 gN plant⁻¹, R² = 0.85). The results showed that randomized Kfold-CV is an undesirable approach. It resulted in more consistent and lower RMSE values during model training and selection, but worse performance on new data. This would be due to information leakage of flight-specific conditions in the validation data split. However, the model predictions were less accurate for data acquired in a different growing season (DBM: RMSEP = 8.50 g plant⁻¹, R² = 0.77; N-uptake: RMSEP = 0.27 gN plant⁻¹, R² = 0.68). Recalibration might solve this issue, but additional research is required to cope with this effect during image acquisition and processing. Further improvement of the model robustness could be obtained through the inclusion of phenological parameters such as crop height.

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in combination with supervised convolutional neural network (CNNs) models have proven successful in making location specific treatments. This site-specific advice limits the amount of herbicide applied to the field to areas that require action, thereby reducing the environmental impact and inputs for the farmer. To develop performant CNN models, there is a need for sufficient high-quality labelled data. To reduce the labelling effort and time, a new labelling method is proposed whereby image subsection pairs are labelled based on their relative differences in weed pressure to train a CNN ordinal regression model. The model is evaluated on detecting weed pressure in potato (Solanum tuberosum L.). Model performance was evaluated on different levels: pairwise accuracy, linearity (Pearson correlation coefficient), rank consistency (Spearman’s (r_s) and Kendal (τ) rank correlations coefficients) and binary accuracy. After hyperparameter tuning, a pairwise accuracy of 85.2%, significant linearity (r_s = 0.81) and significant rank consistency (r_s = 0.87 and τ = 0.69) were found. This suggests that the model is capable of correctly detecting the gradient in weed pressure for the dataset. A maximum binary accuracy and F1-score of 92% and 88% were found for the dataset after thresholding the predicted weed scores into weed versus non-weed images. The model architecture allows us to visualize the intermediate features of the last convolutional block. This allows data analysts to better evaluate if the model “sees” the features of interest (in this case weeds). The results indicate the potential of ordinal regression with relative labels as a fast, lightweight model that predicts weed pressure gradients. Experts have the freedom to decide which threshold value(s) can be used on predicted weed scores depending on the weed, crop and treatment that they want to use for flexible weed control management.

This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific levels. By leveraging open-source Earth observations and socioeconomic data, we develop multiple performance, environmental, and socioeconomic similarity indexes that compare regions based on shared characteristics, such as soil quality, climate, and socioeconomic factors. Using advanced statistical and multi-criteria analysis tools, these indexes are tailored to different stages of agricultural production, enabling region-specific assessments that identify and prioritize the needs for digital solutions across Europe. The results indicate that the developed indexes effectively categorize regions based on comparable characteristics, facilitating the targeted recommendation of ADSs. Additionally, a connectivity performance index is created to assess the local deployment model of agricultural digital solutions (cloud, edge, or mixed), ensuring that the recommendations for technological implementation are feasible and effective given the local connectivity conditions.