Neural Network-Based Automated Soil Salinity Mapping and Remediation Using Wireless Sensor and Cloud Computing

This paper uses neural networks, wireless sensor networks, and cloud computing to automate soil salinity mapping and treatment. Soil salinity affects agricultural output, but conventional techniques of monitoring and managing it are laborious. This system collects real-time soil salinity data from a specific agricultural region using a wireless sensor network. These sensor nodes provide data to a cloud computing platform for automatic soil salinity mapping using a neural network model. The neural network is trained on a large soil property and salinity dataset to accurately predict field salinity distribution. Farmers and land managers may make quick decisions using the cloud-based data storage, processing, and analysis solution. The suggested system incorporates remediation options to inform sustainable soil management. Through field testing, the methodology proves its accuracy and efficiency above older approaches. Precision agriculture producers may monitor soil salinity in real time and take targeted repair actions using the described method. Due to rising agricultural needs and environmental concerns, neural networks, wireless sensor networks, and cloud computing provide a solid foundation for sustainable and technology-driven soil management.