A Review on Smart Crop Selection using Machine Learning and Soil Analytics
Keywords:
Agricultural technology, Climate-based farming, Crop selection, Crop yield prediction, Data-driven agriculture, Machine learning, Precision agriculture, Smart farming, Soil analysis, Sustainable farmingAbstract
Agriculture is essential for food security and economic stability, yet traditional farming relies heavily on intuition rather than scientific analysis, leading to inefficient crop selection. This paper introduces a crop selection system that applies machine learning techniques to analyze soil characteristics and recommend suitable crops. The system evaluates key soil properties such as nitrogen (N), phosphorus (P), potassium (K), pH levels, and climate factors to provide accurate recommendations.
By integrating data analytics, the system assists farmers in making informed decisions, improving crop yield and sustainability. It continuously updates recommendations based on newly acquired data, reducing uncertainty and enhancing efficiency. Additionally, cloud-based data storage allows farmers to track past analyses and soil health trends, enabling precise decision-making. This approach improves crop selection accuracy by utilizing structured datasets, minimizing errors, and optimizing land use.
This system has the potential to transform agricultural decision-making by reducing resource wastage and increasing productivity. Through systematic soil analysis, farmers can improve crop rotation strategies and maintain long-term soil fertility. Future enhancements may include real-time soil monitoring and climate-based modeling to refine accuracy. Expanding the system’s database to include environmental factors such as rainfall patterns and humidity can further improve recommendations. This system aims to be a valuable tool for modern farming, ensuring effective crop management and long-term sustainability.
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