Crop Prediction using NPK Sensor: Novel Approach Using Machine Learning Algorithms

Agriculture, a vital element in nourishing the world’s growing population, faces significant challenges such as climate change, soil degradation, and water scarcity. Accurate crop prediction is essential for maximizing yield and efficient farming practices. However, traditional manual observation methods have limitations in accuracy, scalability, and timeliness. To address these issues, an automated solution integrating NPK sensors emerges as a viable option. These sensors measure key nutrients like Nitrogen, Phosphorus, and Potassium in the soil, providing real-time data on crop nutrition. By feeding NPK sensor data to machine learning models, accurate predictions can be generated. Implementation involves integrating NPK sensors with Raspberry Pi via RS485 to TTL converter and utilizing algorithms like Random Forest and Naïve Bayes for insights on crop suitability. Leveraging edge computing, it provides on-field accuracy, eliminating the need to transport soil samples to testing centers. This integration has the potential to transform modern agriculture, increasing yields while minimizing waste.