Sustainable Crop Recommendation System Using Soil NPK Sensor

Abstract

The effective management of nutrient resources in agricultural practices is crucial for optimizing crop yields and ensuring sustainable farming. Traditionally, farmers have relied on manual methods or expert knowledge to determine the appropriate amount and type of nutrients required by crops. However, these methods often lack precision and can lead to suboptimal fertilization, resulting in reduced productivity and environmental degradation. In recent years, advancements in sensor technology have paved the way for more accurate and efficient crop management systems. One such innovation is the NPK sensor, which enables real-time monitoring of soil nutrient levels. Our proposed system utilizes NPK sensor data to offer personalized fertilization recommendations to farmers. The system integrates sensor technology, machine learning algorithms, and agronomic expertise to provide precise and tailored nutrient recommendations based on the specific requirements of different crops and soil conditions. The system collects data from NPK sensors deployed in the field that includes soil nutrient levels. Machine learning algorithms analyze this data to identify patterns and correlation between nutrient levels and crop performance. By leveraging historical data and agronomic knowledge, the system can generate accurate and timely recommendations for nutrient application. In conclusion, the crop recommendation system presented here offers a novel approach to crop management by leveraging NPK sensor technology and machine learning. By providing accurate and personalized nutrient recommendations, the system has the potential to revolutionize modern agriculture, enhancing productivity while promoting environmental stewardship. Further research and field trials are needed to validate and refine the system’s performance and usability, but the preliminary results show promising potential for the adoption of such system in real-world agricultural settings.