AI-Powered Soil Nutrient Mapping for Optimized Crop Rotation Strategies with Advanced Multispectral Analysis and Precision variable Rate Technology

¹ Department of CS & IT, Kalinga University, Raipur, India
² Research Scholar, Department of CS & IT, Kalinga University, Raipur, India

^* Corresponding author: ku.AnjaliKrushnaKadao@kalingauniversity.ac.in

Abstract

Soil nutrient management is an important factor for the maximum crop production and sustainable agricultural practices. To cater to this need, the research explores the integration of the highest technology on cutting edge AI-powered technologies to boost soil nutrient mapping in tandem with optimising crop rotation strategies. The study apply Variable Rate Technology (VRT), the application of fertilizers that are both precise and adaptive to on going, real time gain on soil it allows savings in fertiliser and reduces wastes. To make data analysis more dynamic and hence more accurate in nutrient predictions, and allow for more responsive management practices, dynamic time warping and gradient boosting machines have been taken into consideration. Socket on MQTT (Message Queuing Telemetry Transport) is ideal for the continuous and real time communication of data between the soil sensors and the decision making platforms to allow for a timely adjustment of the nutrient application. Through Analysis of light in multiple wavelengths known as Multispectral Analysis, a complete overview of soil health is given and specific nutrient deficiencies can be identified and relevant fertilization procedures modified. In addition to this, Sigfox technology also enables long range and reliable communication for the remote soil monitoring, allowing a large scale or geographically dispersed agricultural operations to be manageable. And the proposed approach achieved significant improvement in Nutrient Uptake Efficiency Ratio (NUER) whereas it increased by 7% on average across field zones, Soil Nutrient Variability Coefficient (SNVC) was reduced by 15%, and Soil Nutrient Response Factor (SNRF) has better efficiency in 12%. The findings thus support the successful merging of these technologies to facilitate the improvement of soil nutrient management and support the practice of sustainable agriculture.