Deep learning based Crop Monitoring for effective Agricultural-IoT Management

Achieving sustainable crop production to meet the increasing demand for food depends on the efficient management of agricultural greenhouses. Conventional methods of greenhouse management typically depend on human observation and intervention, resulting in a lack of scalability, efficiency, and precision. In this research, a deep learning technique for managing agricultural greenhouses is proposed by utilizing IoT sensor data obtained from a smart greenhouse. The proposed method aims to enhance crop monitoring and control, hence improving the overall efficiency of greenhouse operations. The utilization of several deep learning models with various activation functions, such as ELU, Swish, ReLU, SELU, and Mish, enables the ongoing surveillance and independent regulation of greenhouse conditions throughout the day. The proposed methodology can be used to enable real-time tracking and autonomous control, leading to resource optimization, increased productivity, and reduced manual intervention. Empirical validation demonstrates the efficacy of utilizing deep learning and IoT in agriculture to improve crop management and sustainability.