Developing an Advanced IoT-Enabled Smart Agriculture Management System to Enhance Crop Growth: A GBRT-Based Approach

An innovation in today's digital age, the internet of things (IoT) enables a wide range of devices to establish connections with one another, automate and control operations remotely, and share data wirelessly. The IoT has several benefits for agricultural framework implementation, including improved crop monitoring and management. A sequential layout is provided for the phases of preprocessing, segmentation, feature extraction, and model training. Applied during the preprocessing phase to enhance photographs of varying sizes and reduce noise in RGB images. The model is able to correctly identify diseased plant leaves in images by employing iterative computation. The term for this method is leaf image segmentation. The efficient and simple K-means algorithm has seen heavy use in feature extraction. This model was trained using a TL-BiGRU-CRF method. Compared to state-of-the-art CRF and BiGRU, this approach looks dated. With an astounding accuracy percentage of 97.65%, the data clearly indicate a considerable enhancement.