Intelligent Farming in Rural Areas: CBGRU-Based Smart Agriculture and Precision Solutions

Climate-Smart Agriculture (CSA) aims to reduce food insecurity, boost rural economies, and protect the environment. CSA programs sometimes ignore small-scale farmers' demands, who face higher challenges. This research introduces Vulnerable-Smart Agriculture (VSA) to address small-scale farmers' needs and create a sustainable farming framework. The recommended method involves preprocessing, feature selection, and model training. Data cleansing, normalization, and correlation analysis are preprocessing steps. MRMR and CFS are used to select features. The CBGRU architecture trains the model. The CBGRU model outperforms CNN and BiGRU with an average accuracy of 93.60%. High accuracy shows that the VSA strategy meets small-scale farmers' needs through data-driven insights. VSA can cover the CSA gap by including small-scale farmers, according to this research. Strong preprocessing, feature selection, and CBGRU-based model training can boost agricultural production, sustainability, and rural lives.