Soil Fertility Prediction

Abstract

Abstract: Soil fertility plays a important role in figuring out agricultural productiveness and sustainability. Traditional methods of assessing soil fertility involve time-consuming and high-priced laboratory tests, restricting their scalability and real-time applicability. To overcome these challenges, this study proposes a data-driven method utilizing machine learning techniques for accurate and efficient soil fertility prediction. Several machine learning algorithms, inclusive of decision trees, Random Forests, k nearest neighbors, and Gradient Boosting Machines (GBM), are employed to model the complex relationships among soil properties and fertility. Feature selection techniques are carried out to identify the most influential soil parameters for enhanced prediction accuracy and reduced model complexity.The outcomes demonstrate that machine learning models can appropriately predict soil fertility, outperforming traditional approaches in terms of speed and cost-effectiveness. Moreover, the characteristic selection process identifies key soil properties that have the most significant effect on fertility, offering valuable insights for agricultural decision-making and targeted soil management. The proposed approach offers potential applications in precision agriculture, enabling farmers to make knowledgeable choices regarding crop selection, nutrient management, and irrigation strategies based totally on actual-time soil fertility predictions. By optimizing resource allocation and minimizing environmental influences, this data-driven solution contributes to the promotion sustainable agricultural practices and guarantees food safety for a growing global population. These are the essential nutrients that the crop requires for its growth pH nitrogen(N), phosphorus(P), potassium(K), CaCo3, Organic Carbon, Organic matter, CEC (Cation exchange capacity) .