Artificial Intelligence Approaches for Analyzing and Interpreting Visual Data in Plant Biology

The integration of advanced imaging technologies in plant science is reshaping the way we monitor and manage agricultural systems. This chapter explores the significance of image-based data in modern plant science, emphasizing the role of precision agriculture in improving crop health, disease detection, and growth monitoring. It delves into techniques for phenotypic trait extraction, such as automated feature detection and the use of computer vision for analyzing morphological traits. The application of deep learning in disease classification and stress identification enables early detection of both abiotic and biotic stress factors. Moreover, 3D plant modeling, time-series analysis, and multispectral imaging are discussed as powerful tools for assessing plant health and growth dynamics. The chapter also covers the role of data augmentation, synthetic data generation, and transfer learning in enhancing model performance. Additionally, the integration of IoT and robotics in smart farming is explored, highlighting the advancements in autonomous systems and real-time monitoring.