Analysis of the data processing method and system design of the IoT hardware gateway using a microcontroller

Precision agriculture utilizes sensors, controllers, and intelligent data processing and analysis to guarantee ideal increasing conditions for crop production. These technologies give agricultural businesses the ability to precisely monitor and make decisions. The Internet of Things (IoT) has further strengthened this field by allowing real-time communication between devices, ensuring that farmers can monitor soil conditions, water levels, and crop necessities instantly. Despite the technological advancements provided over current microcontrollers, there are still notable limitations. To moderate these limitations, this research introduces a Bird Swarm-Intelligent Twin Support Vector Machine (BS-TSVM) method for modeling soil moisture prediction. The BS-TSVM method is designed to leverage the optimization capacity of swarm intelligence while maintaining the classification strength of Twin Support Vector Machines (TWSVM). Within the framework, Min-Max normalization is employed to process and restructure raw sensor data into consistent formats, whereas the most important characteristics are extracted using Linear Discriminant Analysis (LDA), which lowers dimensionality and increases processing efficiency. All these processes are implemented within an IoT-enabled agricultural field. Experiments conducted on soil moisture sensor readings using Python 3.11 demonstrate that BS-TSVM achieves high predictive accuracy, with an R² value of 0.99, clearly outperforming existing predictive models. The holistic BS-TSVM framework is therefore intended not only to improve predictive accuracy in modeling soil moisture but also to add a strong design approach for developing IoT hardware gateways in resource-constrained microcontroller systems, promoting sustainable and intelligent farming practices.