Data-Driven Cloud Based Soil Nutrient Prediction System for Precision Agriculture
Amit Mishra, Jeevesh Dass, Siddhant Saxena, Ishan Satya Prakash, Sargun, Arshia Garg, Karun Verma, Jhilik Bhattacharya · 2024
Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine learning-based model that will predict the NPK fertilizer to achieve the higher crop yields. Field sensor data along with meteorological data and remote sensing data from various test fields are integrated. A random forest and decision tree machine learning model are applied to the integrated data to develop a software tool for precision agriculture, enabling accurate estimation of nutrient requirements. A mobile application is used to share the nutrient prediction with farmers, enabling them to make well-informed choices regarding resource utilization efficiency and environmental friendliness. This research can transform agricultural practices globally and contribute towards food security on earth.