From Field to Cloud: IoT and Machine Learning Innovations in High-Throughput Phenotyping

High-throughput phenotyping in plant sciences is undergoing a transformative change, driven in part by the integration of the Internet of Things (IoT) and Machine Learning (ML). This paper reviews an end-to-end IoT architecture designed to enhance high-throughput phenotyping by leveraging IoT’s real-time data capture and ML’s advanced analytical capabilities. Our focus extends to scalable and intelligent data collection methods, network connectivity, and the application of smart phenotyping techniques. We provide a comprehensive analysis that highlights both the current challenges and potential future innovations in IoT and ML within this context. Critical issues related to devices, network frameworks, ML models, and platforms are explored. This review contributes valuable insights into the rapidly evolving domain of high-throughput phenotyping in plant sciences, showcasing the emerging trends and future prospects.