Smart Farming in Rural Landscapes: Leveraging Machine Learning for Sustainable Agricultural Transformation

Abstract This study aims to systematically evaluate machine learning (ML) applications in rural agricultural contexts, a critical yet underrepresented area in the literature. Unlike prior reviews focusing on high-tech farming environments, this research uniquely centers on smallholder and resource-constrained systems to explore the intersection of ML, sustainable agriculture, and rural development. A Systematic Literature Review (SLR) was employed, following PRISMA guidelines, and drawing from peer-reviewed databases including Scopus, Web of Science, and IEEE Xplore. The analytical process encompassed five structured stages: data importation, descriptive analysis, interactive visualization, linkage analysis, and insight extraction, ensuring analytical rigor and replicability. The results reveal that although ML technologies such as CNNs, SVMs, and LSTM networks are increasingly used for crop monitoring, disease detection, and irrigation management, their deployment remains predominantly confined to well-resourced agricultural systems. Rural applications face persistent challenges, including limited digital infrastructure, data scarcity, and low digital literacy. Moreover, digital systems have improved rural education processes, showing potential for broader agricultural applications. This study contributes by identifying methodological trends and context-specific gaps, offering a roadmap for developing adaptable, low-cost ML solutions. Limitations include the exclusion of non-English and non-open-access literature and potential biases in database indexing. In conclusion, to realize the full potential of ML in transforming rural agriculture, future research should prioritize inclusive technology design, interdisciplinary collaboration, and policy support. Such efforts are vital to achieving equitable and sustainable food systems in alignment with global development goals.