Using trained deep learning models on an out-of-distribution dataset could affect those models’ performance, which is most likely to decline due to the domain shift between the training domain and the target domain. To this end, Domain Generalization (DG) is one of the tools used to improve the generalization rate of the model and increase its adaptability to unseen data. Using a data-based domain generalization, this study intends to create a solar module segmentation model using thermal UAV images, by including various type of technologies and representations of solar modules in training data. Developing the generalization of the solar panel segmentation could enhance the performance in the workflow of automatic photovoltaic plants monitoring. The thermal images dataset was collected from six photovoltaic sites in Benguerir - Morocco in various shooting conditions. The global capacity of these systems is 1MW and they are equipped with different technologies. The training of this model achieved 99% [email protected] for testing and could perform well on unseen data, that is different from the training data, ensuring the effectiveness of the approach to generalize the trained Deep learning models which can be used in other domains.

One of the foremost answers to the challenge of food security lies in precise prediction of crop yields. This prediction task is complex due to the numerous variables involved. A primary source for yield prediction is historical remote sensing data, notably satellite imagery. However, such imagery often includes redundant data because of its multiple spectral bands and the extensive archived historical data over extended periods. In this study, we introduce an advanced framework for corn yield prediction at field level utilizing Long Short-Term Memory (LSTM) combined with attention mechanisms and randomized Principal Component Analysis (r-PCA). This framework aims to filter out redundant data and improve yield prediction using raw Sentinel-2 imagery, and incorporating stacked temporal and spectral data. Our model was tested using data of the year 2019, and was compared against the simple LSTM architecture using key performance indicators. Our proposed framework achieved a Mean Absolute Error (MAE) of 1.40, surpassing the MAE recorded by the LSTM. The results validate the effectiveness of our approach, particularly for field-scale predictions, demonstrating the enhanced capability of our model in accurately forecasting crop yields at this scale.