Agrivoltaic systems (AV) combine agricultural activities with the production of electricity from photovoltaic (PV) panels on the same land area. The concept of AV systems was introduced in 1982 by Goetzberger and Zastrow, but only more recently have the increased environmental concerns and the favorable economic and political frameworks stimulated a growing interest in this technology. A critical issue in the development of AV is the selection of crops that can grow profitably under the micrometeorological conditions generated by AV systems. This experiment studied the effect of four different shade depth treatments (AV1 = 27%, AV2 = 16%, AV3 = 9%, and AV4 = 18%) on the morphology, physiology, and yield of a soybean crop grown under a large-scale AV system. The field results were used to validate the output of a simulation platform that couples the crop model GECROS with a set of algorithms for the estimation and spatialisation of the shading, radiation, and crop-related outputs. Crop height, leaf area index (LAI), and specific leaf area (SLA) all increased under the most shaded AV areas compared to the full light (FL, control) conditions. On average, under an AV system, the grain yield and the number of pods per plant were reduced by 8% and 13%, and in only one area (AV2) was a slight increase in grain yield (+4.4%) observed in comparison to the FL. The normalised root mean square error (nRMSE) value of the predicted grain yield differed from the observed grain values of 12.9% for the FL conditions, 15.7% in AV1, 16.5% in AV2, 6.71% in AV3, and 2.82% in AV4. Although the model simulated the yield satisfactorily, the results of the RMSE revealed that the model tends to underestimate the yield with an increase in shade, particularly for the AV1 and AV2 conditions.

Timely yield prediction is crucial for the agri-food supply chain as a whole. However, different stakeholders in the agri-food sector require different levels of accuracy and lead times in which a yield prediction should be available. For the producers, predictions during the growing season are essential to ensure that information is available early enough for the timely implementation of agronomic decisions, while industries can wait until later in the season to optimize their production process and increase their production traceability. In this study, we used machine learning algorithms, dynamic and static predictors, and a phenology approach to determine the time for issuing the yield prediction. In addition, the effect of data reduction was evaluated by comparing results obtained with and without principal component analysis (PCA). Gaussian process regression (GPR) was the best for predicting maize yield. Its best performance (nRMSE of 13.31%) was obtained late in the season and with the full set of predictors (vegetation indices, meteorological and soil predictors). In contrast, neural network (NNET) and support vector machines linear basis function (SVMl) achieved their best accuracy with only vegetation indices and at the tasseling phenological stage. Only slight differences in performance were observed between the algorithms considered, highlighting that the main factors influencing performance are the timing of the yield prediction and the predictors with which the machine learning algorithms are fed. Interestingly, PCA was instrumental in increasing the performances of NNET after this stage. An additional benefit of the application of PCA was the overall reduction between 12 and 30.20% in the standard deviation of the maize yield prediction performance from the leave one-year outer-loop cross-validation, depending on the feature set.

Unmanned aerial vehicle (UAV) remote sensing was used to estimate the leaf area index (LAI) and leaf chlorophyll content (LCC) of two hemp cultivars during two growing seasons under four nitrogen fertilisation levels. The hemp traits were estimated by the inversion of the PROSAIL model from UAV multispectral images. The look-up table (LUT) and hybrid regression inversion methods were compared. The hybrid methods performed better than LUT methods, both for LAI and LCC, and the best accuracies were achieved by random forest for the LAI (0.75 m² m⁻² of RMSE) and by Gaussian process regression for the LCC (9.69 µg cm⁻² of RMSE). High-throughput phenotyping was carried out by applying a generalised additive model to the time series of traits estimated by the PROSAIL model. Through this approach, significant differences in LAI and LCC dynamics were observed between the two hemp cultivars and between different levels of nitrogen fertilisation.

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using 15 VIs time series, and predicted yield using peak descriptors derived from these VIs time series with root mean square error of 2.3 Mg DM ha⁻¹. The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.

Miscanthus is a leading perennial biomass crop that can produce high yields on marginal lands. Moisture content is a highly relevant biomass quality trait with multiple impacts on efficiencies of harvest, transport, and storage. The dynamics of moisture content during senescence and overwinter ripening are determined by genotype × environment interactions. In this paper, unmanned aerial vehicle (UAV)‐based remote sensing was used for high‐throughput plant phenotyping (HTPP) of the moisture content dynamics during autumn and winter senescence of 14 contrasting hybrid types (progeny of M. sinensis x M. sinensis [M. sin x M. sin, eight types] and M. sinensis x M. sacchariflorus [M. sin x M. sac, six types]). The time series of moisture content was estimated using machine learning (ML) models and a range of vegetation indices (VIs) derived from UAV‐based remote sensing. The most important VIs for moisture content estimation were selected by the recursive feature elimination (RFE) algorithm and were BNDVI, GDVI, and PSRI. The ML model transferability was high only when the moisture content was above 30%. The best ML model accuracy was achieved by combining VIs and categorical variables (5.6% of RMSE). This model was used for phenotyping senescence dynamics and identifying the stay‐green (SG) trait of Miscanthus hybrids using the generalized additive model (GAM). Combining ML and GAM modeling, applied to time series of moisture content values estimated from VIs derived from multiple UAV flights, proved to be a powerful tool for HTPP.