AI-Based Nutrient Management System For Crops

This study introduces a novel method for predicting crop yields by utilizing artificial intelligence (AI) and machine learning (ML) techniques. By employing sophisticated algorithms and extensive datasets, this model seeks to provide accurate forecasts of crop yields, which can assist farmers, policymakers, and other stakeholders in making well-informed decisions. The integration of AI technologies allows us to tackle the complexities present in agricultural systems, such as environmental variability, soil conditions, and pest pressures. Our methodology employs ML algorithms to examine historical yield data, weather patterns, soil characteristics, and other pertinent factors to produce precise predictions for future crop yields. The AI-driven crop yield prediction framework proposed here serves as a valuable tool for boosting agricultural productivity, optimizing resource use, and reducing risks linked to crop production. This study emphasizes the transformative impact of AI in agriculture, showcasing its potential to lessen uncertainties, enhance productivity, and support a sustainable agricultural future. By connecting technology with traditional farming practices, the proposed framework not only improves decision-making but also addresses pressing global issues like food insecurity, resource scarcity, and environmental degradation.