The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.

2'-Deoxycitidine (dCyd) and 2'-deoxyguanosine (dGuo) were subjected to reaction with phenylglycidyl ether (PGE) in methanol in order to study the formation of the corresponding 2'-deoxynucleoside adducts. Separation methods were developed on analytical and semi-preparative scales using high-performance liquid chromatography with photodiode-array detection on a reversed-phase column and on a polystyrene-divinylbenzene column. The use of the latter column was prompted by decomposition of the preparatively isolated dGuo-PGE adducts on the reversed-phase column. The use of a polystyrene-divinylbenzene column solved this problem and also revealed the presence of one more peak in both the dCyd- and dGuo-PGE reaction mixtures. The adducts of dCyd and dGuo were isolated on preparative reversed-phase and polystyrene-divinylbenzene columns and characterized by UV, fast atom bombardment mass and 360 MHz 1H NMR spectrometry. The adducts of dCyd were the diastereomers of N-3-(2-hydroxy-3-phenoxypropyl)-2'-deoxycytidine and N4-(2-hydroxy-3-phenoxypropyl)-2'-deoxycytidine whereas those of dGuo were the two diastereomers of N-7-(2-hydroxy-3-phenoxypropyl)-2'-deoxyguanosine and a third peak which appeared to be mainly N2-(2-hydroxy-3-phenoxypropyl)-2'-deoxyguanosine.

Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are key vegetation indices for modeling energy and mass exchange between the atmosphere and land surfaces and can therefore be utilized for yield prediction. Consequently, suitable methods have been developed to retrieve LAI and LCC from remotely sensed data. Among these, the inversion of Radiative Transfer Models stands out as a promising approach, as it addresses the issue of limited transferability and minimizes the need for extensive field measurements also accounting for crop variability.The objective of this study is to assess the applicability of the Soil Canopy Observation of Photochemistry and Energy Fluxes (SCOPE) model for estimating LAI and LCC of potato (Solanum tuberosum L.) using time series of hyperspectral images captured by an uncrewed aerial vehicle. A field experiment was conducted in Belgium from June to October 2024, involving two potato varieties, early and late, subjected to two nitrogen fertilization levels and six different biostimulants. Throughout the crop growth cycle, hyperspectral UAV images were captured biweekly using the Specim AFX10 camera. On the same days, in situ measurements of LAI and LCC were performed. LAI and LCC were estimated using a look-up table (LUT) approach based on the inversion of the SCOPE model. A cost function (norm2 distance) was employed to sort the LUT and identify a set of spectra that minimized the distance between measured reflectance and simulated reflectance in the LUT. The estimated LAI and LCC values were then compared with their corresponding in situ measurements.Preliminary results indicate that simulated LAI and LCC showed potential for use in designing models to predict measured LAI and LCC (R2=0.26 and R2=0.30, respectively, p

Rapid advancements in technology, particularly the rise of artificial intelligence (AI) and the integration of uncrewed aerial vehicles (UAVs) equipped with RGB, multi- and hyperspectral sensors, have boosted agricultural research on crop disease detection. This has led to a surge in studies exploring high-technology approaches to detecting crop diseases. While numerous studies have demonstrated high accuracy in detecting specific diseases or pests in crops, concerns arise regarding their reproducibility and generalisability.We conducted a meta-analysis of over 100 research papers to examine how models are trained and validated, with a focus on how datasets for training, validation and testing were handled. In principle, a model can only be considered robust and widely applicable if it performs well on an entirely new dataset, i.e., a dataset it wasn’t specifically trained one. Otherwise, AI models risk overfitting to specific datasets or fields, potentially detecting signals that are not universal or not related to the targeted pest or disease. This issue arises when datasets are randomly split in training, validation and test subsets.Our analysis revealed significant limitation in current practices. Nearly half of the reviewed papers relied on a single dataset (one single field, one single flight) for both model training and validation. About one-quarter of the studies used data from a single field with repeated flights during the same growing season. Only another quarter utilized datasets from multiple fields; however, the majority of these studies still used a random split for training and testing, meaning their models were not evaluated on independent datasets. In addition, a handful of studies using RGB data, applied transfer learning, with models pretrained on public (non-UAV) datasets and then applied to UAV datasets.Overall, only 10% of the reviewed papers validated their models on fully independent datasets, i.e, using transfer learning or using an independent (untrained) separate field to test the model. We found that particularly models constructed with multispectral or hyperspectral data did not use independent datasets. On top of that, none of the studies explicitly tested whether their models were pest- or disease-specific, i.e., whether the models were sensitive only to the pest or disease they were trained to detect.These findings highlight a critical limitation in the robustness and scalability of current AI-approaches to crop disease detection with UAVs. To address this, we call on researchers to include independent test datasets in their studies, and urge journals and reviewers to prioritize this criterion during evaluations. Additionally, we advocate for the public sharing of datasets to enable the development of robust and generalisable methods.