Optimizing Smart Irrigation in Agriculture with a Fuzzy Logic and Genetic Algorithm Hybrid Model for Water Resource Management

Smart irrigation is crucial for managing agricultural water resources in the face of climate change and extreme weather events. Efficient management is crucial in light of the water scarcity. Food safety and long-term agricultural development are both enhanced by smart farming systems that combine adaptive and mitigation techniques. Water conservation is aided by wireless networks, which increase irrigation efficiency. Included in the proposed system are operations for preprocessing data, selecting features, and training models. Normalization, standardization, imputation of missing data, and outlier elimination are all part of data preparation. Boruta feature selection keeps only the most crucial elements by eliminating the rest. The GAFLC model can forecast SIS performance using either fuzzy rules for classification or rough sets for feature selection. Fuzzy classification methods are optimized by adaptive evolutionary algorithms. Improving model performance, the AUC is $94.34 \%$, specificity is $97.18 \%$, recall is $98.52 \%$, and accuracy is $96.43 \%$. Based on these findings, pre-emption is clearly the way to go. In order to better manage agricultural resources and conserve water, the GAFLC model employs cutting-edge data processing and machine learning algorithms to enhance irrigation efficiency.