Applying machine learning to soil analysis for accurate farming

Abstract

A crucial component of agriculture is soil. Soil analysis is critical for optimizing agricultural practices and ensuring sustainable crop production. Traditional methods are often time-consuming and labor intensive, limiting their scalability and real-time applicability. The application of machine learning techniques in soil nutrient analysis has emerged as a promising solution. There is a lot of complicated soil data, but algorithms that use machine learning can handle it all, enabling accurate prediction and assessment of soil nutrient content. Integration with remote sensing technologies enhances the capabilities of soil nutrient analysis, allowing for rapid assessment at different scales. Machine learning facilitates personalized recommendations for fertilizer application, irrigation strategies, and soil amendments, tailored to the specific needs of learning and adaptive individual fields or crops. The continuous capabilities of machine learning models ensure up-to-date nutrient management recommendations. Challenges include the availability of representative interpretability of models. Nevertheless, the integration of machine learning in soil nutrient analysis offers improved resource utilization, enhanced crop productivity, and sustainable soil management practices. Ongoing research and collaboration with domain experts will further advance the application of machine learning in this field.

Keywords

• soil type
• crop type
• pesticides usages
• number of doses
• number of weeks
• number of weeks quit
• season
• yield damage