AI-Based Soil Fertility Management Review: Challenges And Opportunities

Abstract

Agriculture is vital for global food security but faces challenges meeting growing demand. Soil fertility management is a major challenge, impacting crop yields and environmental sustainability. Effective strategies providing timely soil nutrient information and fertilizer recommendations are needed. Machine learning offers promise in analyzing large datasets to predict soil properties and crop yield based on factors like nutrient levels, soil pH, and climate conditions. AI-based soil fertility management can improve crop yield, reduce fertilizer waste, and minimize environmental impact but requires accurate data on soil nutrients, weather conditions, and other relevant factors for training machine learning models. In conclusion, AI-based soil fertility management offers a promising solution to the challenges of meeting growing agricultural demands while ensuring environmental sustainability. By using machine learning to analyze large datasets and provide fertilizer recommendations, this approach has the potential to improve crop yield, minimize fertilizer waste, and reduce environmental impact. However, its success relies heavily on accurate and comprehensive data for training machine learning models. As technology advances, the integration of AI in soil fertility management holds great opportunities for sustainable agriculture.