Abstract:
The efficient management of soil fertility and optimized crop selection are crucial factors in modern agriculture, impacting both agricultural yield and environmental sustainability. This project introduces an NPK Soil Fertility Measurement System integrated with a Crop Recommendation System utilizing Machine Learning techniques. The NPK Soil Fertility Measurement System employs advanced sensor technologies to accurately assess the levels of essential nutrients - Nitrogen (N), Phosphorus (P), and Potassium (K) - in the soil. the system aims to provide real-time and accurate information about the soil's nutrient content, enabling farmers to make informed decisions regarding soil treatment and nutrient supplementation.
Building upon the soil fertility data collected, the Crop Recommendation System employs state-of-the-art Machine Learning algorithms. These algorithms analyze historical crop performance data, along with the current soil nutrient composition, climate conditions, and other relevant factors, to generate tailored crop recommendations for specific plots of land.
This approach helps maximize agricultural productivity while minimizing resource waste and environmental impacts.
