The current research landscape in agricultural and environmental monitoring is witnessing a significant shift towards leveraging advanced AI and machine learning techniques to address complex challenges. Innovations are primarily focused on enhancing the precision and efficiency of agricultural practices through data-driven insights. Key areas of development include the integration of multi-modal data sources, such as remote sensing imagery, meteorological data, and farm management practices, to improve crop yield predictions and soil health assessments. Additionally, there is a growing emphasis on automating and optimizing traditional agricultural metrics, such as leaf area index estimation, using UAV imagery and machine learning models. These advancements not only promise to revolutionize precision agriculture but also contribute to sustainable farming practices and climate resilience. Notably, the field is also exploring the potential of citizen science and superpixel segmentation methods to enhance deforestation detection and monitoring efforts. Overall, the integration of AI with agricultural and environmental sciences is paving the way for more informed, sustainable, and scalable solutions.