Developemnt of an Iot-Based Sensor for Realtime Analysis and Nutrient Management in Maize (zea Mays) Cultivation

The Development of a smart farm management system helps to improve productivity and proceeds in farming. The system integrates machine learning algorithms to recommend suitable fertilizers based on the nutrients lacking in the soil. The key components of the system include an ESP 32 MCU and an NPK sensor, which work together to collect soil data. The collected data is then transmitted to a server for analysis and processing using machine learning models. This system's primary goal is to help farmers make well- informed decisions on the management of nutrients for their maize crops. The system's objectives are to maximize crop output, optimize fertilizer use, and increase overall farm sustainability by utilizing real-time data collecting and analysis. The system can now offer precise and customized fertilizer recommendations based on the unique nutritional needs of the plant thanks to the use of machine learning. The system makes sure that the ESP 32 MCU, the central server, and the sensor nodes are all in constant communication with each other thanks to the Internet of Things. Farmers can now obtain up-to-date data on soil nutrient levels and practical advice for efficient nutrient management techniques. All things considered, this Internet of Things (IoT) sensor system provides a complete answer for raising the effectiveness of maize production and encouraging sustainable farming methods