Design Of An Iterative Method For Optimizing Agricultural Productivity And Crop Quality Using Graph-Based Q-Learning And Nutrient-Action Frameworks

Abstract

Agricultural sustainability and productivity are paramount for feeding the ever-growing global population under fluctuating environmental conditions & scenarios. Traditional fertilization and crop management strategies often fall short in optimizing nutrient usage and enhancing crop quality due to their generalized nature and inability to adapt to local environmental changes. Current methodologies lack precision in addressing the intricate dependencies between various nutrients and environmental factors affecting crop growth and quality. In response to these limitations, this study introduces a novel approach utilizing graph-based Q-learning, applied in two distinct but complementary domains: Nutrient Level Evaluation and Fertilizer Recommendation, and Crop Quality Prediction and Improvement Scenarios. The core of our methodology lies in the construction of Nutrient-Action and Quality-Action Graphs, where nodes represent the quantifiable levels of soil nutrients or crop quality parameters, and edges symbolize actionable measures such as specific fertilizer applications or farming practices adjustments. Our proposed models leverage the flexibility and learning capability of Q-learning, a form of reinforcement learning, to navigate the constructed graphs efficiently. This enables the algorithm to discern optimal strategies by iteratively learning from the environment and updating policies based on the observed outcomes, thus accommodating the dynamic nature of agricultural systems. The nutrient-action graph model focuses on optimizing the amount of each critical nutrient (e.g., nitrogen, phosphorus, potassium) tailored to the specific crop and soil types, considering environmental factors like temperature and humidity. Simultaneously, the quality-action graph model predicts and improves crop quality by recommending adjustments in farming practices based on historical data, environmental conditions, and existing agricultural practices. The integration of these models into the agricultural decision-making process represents a significant advancement over traditional methods. Preliminary results suggest that our approach could lead to a 10-20% increase in crop yield and quality, outperforming conventional fertilization techniques. Furthermore, our crop quality improvement strategies, informed by predictive analytics, indicate potential enhancements in crop nutritional content and disease resistance by 5-15%, contingent upon the agricultural context and interventions employed in real-time scenarios. This work impacts the agricultural sector by providing a scalable, data-driven framework for personalized crop management and fertilization strategies. By harnessing the power of Q-learning and graph-based representations, we offer a sophisticated tool for enhancing food security, minimizing environmental impacts, and promoting sustainable farming practices worldwide.