Smart ShetiSarthi

Agriculture remains the backbone of many developing nations, providing livelihoods and en suring food security for millions. However, farmers continue to face persistent challenges such as unpredictable weather conditions, soil degradation, pest infestations, nutrient imbal ance, and limited access to modern technologies. Traditional farming methods often rely on experience-based decision-making, which may not always be reliable in dynamic environmen tal conditions. To overcome these challenges, there is an increasing demand for intelligent, technology-driven systems that can assist farmers in making precise and timely agricultural decisions. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have opened new frontiers in smart agriculture. These technologies enable automated disease detection, soil quality assessment, crop recommendation, and real-time field monitoring. While numerous studies have explored AI-based solutions for specific agricultural problems, most existing systems focus on individual aspects rather than delivering a unified, end-to-end platform. This survey presents a comprehensive review of six key research contributions related to crop disease prediction, soil classification, genomic-based disease resistance, and intelligent agricultural applications. The analysis highlights their methodologies, strengths, and limita tions, while identifying existing research gaps. Based on these insights, the study proposes a conceptual multi-model architecture designed to integrate various predictive modules into a single, cohesive smart agriculture framework for future implementation