Internet of things driven multilinear regression technique for fertilizer recommendation for precision agriculture

Abstract Food instability has been linked to infertility, health issues, accelerated aging, incorrect insulin regulation, and more. Innovative approaches increased food availability quality. Agriculture environment monitoring systems need IoT machine learning. sensors provide all necessary data for agriculture production forecast, fertilizer management, smart irrigation, crop monitoring, disease diagnosis, pest control. Precision may boost yields by prescribing the right water-fertilizer-paste ratio. This article presents IOT based recommendation system Smart agriculture. framework uses devices acquire agriculture-related data, then learning is applied suggest in correct quantity at appropriate time. The acquisition phase collects input including soil temperature, moisture, humidity, regions' weather details. Features are selected using Sequential Forward Floating Selection algorithm. Multilinear Regression performs classification. performance of SFSS-MLR compared Random Forest, C4.5, Naïve Bayes SFSS MLR better accuracy, precision, recall F1. accuracy 99.3 percent.