The field of agricultural research is moving towards the development of innovative solutions for sustainable agricultural productivity, driven by the increasing demand for food security and the need to mitigate the impacts of climate change. Recent studies have highlighted the potential of foundational models, such as those used in remote sensing and climate sciences, to be applied to agricultural monitoring tasks like crop type mapping, crop phenology estimation, and crop yield estimation. Furthermore, the integration of artificial intelligence, robotics, and hyperspectral imaging is showing promising results in areas like real-time weed detection, canopy-aware spraying, and crop yield prediction. Noteworthy papers in this area include:

• A study on the development of a robotic system with AI for real-time weed detection and canopy-aware spraying, which demonstrated high accuracy and efficiency in indoor trials.
• A comprehensive survey and comparative study of hyperspectral anomaly detection methods, which highlighted the strengths and limitations of different approaches and identified deep learning models as the most accurate.
• A proposal of a dynamic fusion framework for crop yield prediction, which achieved state-of-the-art results in experiments on multi-year datasets.
• The introduction of a multi-source hyperspectral dataset for global vegetation trait prediction, which enabled the development of robust cross-domain methods for plant trait prediction.