Abstract—Global rice production surpasses half a billion tons, with Asia at the forefront. However, the Philippines continues to be a significant importer, representing six percent of global imports. The country faces considerable obstacles in domestic rice production, such as the necessity for cost-effective labor, consistent rainfall, efficient land management, irrigation infrastructure, and appropriate soil conditions. Insufficient government support intensifies these issues. To improve crop production and reduce dependence on imports, modern agricultural methods, such as smart farming technologies, are crucial. In light of these challenges, this research emphasized the vibrant role of rice farmers in the Philippines’ economy. The study employed data from rice farmers registered in the “Ani at Kita: Registry System for Basic Sectors in Agriculture (RSBSA).” The Classification and Regression Trees (CART) algorithm was utilized to autonomously select qualifying farmers for incentives in each barangay. Every farmer was regarded as a distinct data point, characterized by attributes including registration status, revenue source, farming activities, household size, and involvement in beneficiary programs. The algorithm employed a hierarchical framework to classify farmers into smaller groups according to dynamic variables such as Gini impurity or entropy, finally ranking them in leaf nodes. Web-based technology was likewise employed in to the application solution to facilitate data interchange between barangays and local government bodies. The system garnered favorable feedback, achieving an average rating of 4.59 out of 5, signifying strong endorsement from respondents.
 
Keywords—Classification and Regression Trees (CART) algorithm, rice, rice farming, rice production, local government assistance, Ani at Kita: Registry System for Basic Sectors in Agriculture (RSBSA)


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