Deep Learning for Real-Time Plant Monitoring Systems

This chapter explores the integration of deep learning algorithms in real-time plant monitoring systems, a departure from traditional observational methods to intelligent, automatic monitoring. Using artificial intelligence (AI) models such as Vision Transformers, 3D convolutional neural networks, neural ordinary differential equations, and graph neural networks, advanced systems can interpret complex plant behaviors, stress responses, and growth patterns at high accuracy rates. Multimodal sensor fusion of RGB, hyperspectral, light detection and ranging (LiDAR), and bio-signals enable robust biotic and abiotic stress detection in diverse conditions. Advances in Edge AI, TinyML, and federated learning for scalable and field-level deployment are also explored. Synthetic plant simulators, digital twins, and explainable AI frameworks enhance the transparency and predictive ability of the systems. Ethical concerns, biodiversity protection, and sustainable AI are emphasized throughout. Real-world test cases and benchmarking provide some insight into real-world applications as well as future directions, such as AI-based closed-loop systems in space agriculture. The chapter formulates a revolutionary platform for intelligent, resilient, and sustainable plant ecosystem monitoring.