Smart Irrigation System to Minimize Water Usage in Agriculture Using Fully Connected Neural Networks

The pillar for effective farming is water management in irrigation, especially where growing water scarcity challenges are receiving much attention. This paper introduces a smart irrigation system whose water use remains optimized through the FCNN model when soil moisture conditions become critical. The data used in the experiments included temperature, pressure, and altitude readings that match the soil moisture levels, hence reporting remarkable improvements in accuracy and other aspects of performance. The accuracy of the FCNN model is 95% because it correctly classifies test cases into the validation set. The SVM model yielded an accuracy of 92%, showing its weakness in extracting the nonlinear relation in the data. Moreover, the precision and recall concerning some classes were fairly weak in the SVM model. With this work, the possibility of neural networks in revealing inherent nonlinear relationships characteristic of the data involved in agriculture applications thus justifies the superior performance of the FCNN approach toward the design of precision irrigation systems. This paper thus finally highlights the promise of scalable solutions to boost efficiency in water usage in agriculture as much as possible for sustainable agricultural practice.