The recent advancements in agricultural technology have significantly leveraged the integration of machine learning and computer vision to automate and enhance various aspects of crop management. A notable trend is the use of drones and robots for high-throughput phenotyping, enabling precise monitoring and analysis of crop health and maturity. For instance, the development of machine learning models for predicting soybean maturity and cranberry ripening using drone imagery has shown promising results, offering scalable and objective assessment methods. Additionally, the application of deep learning models for automatic detection and counting of grape bunches and coconut palm trees has demonstrated the potential for automating yield estimation and agricultural planning. Another area of innovation is the use of synthetic data for training models to detect fruit deformities and anomalies, addressing the challenge of data scarcity in agricultural scenarios. Furthermore, the automated classification of ozone exposure in plants through electrical signals represents a novel approach to environmental monitoring. These developments collectively underscore the transformative impact of AI and robotics in modern agriculture, paving the way for more efficient, accurate, and sustainable farming practices.