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

• Published in: SN applied sciences Vol. 5; no. 10; pp. 264 - 9
• Main Authors: Kollu, Praveen Kumar, Bangare, Manoj L., Hari Prasad, P. Venkata, Bangare, Pushpa M., Rane, Kantilal Pitambar, Arias-Gonzáles, José Luis, Lalar, Sachin, Shabaz, Mohammad
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-10-2023; Springer Nature B.V; Springer
• Subjects: Agricultural production; Agriculture; Algorithms; Applied and Technical Physics; Chemistry/Food Science; Classification; Crop diseases; Crop yield; Crops; Data acquisition; Digital agriculture; Earth Sciences; Engineering; Environment; Fertilizer quantity management; Fertilizers; Food availability; Food quality; Infertility; Insulin; Internet of Things; Learning algorithms; Machine learning; Materials Science; Meteorological data; Moisture effects; Monitoring; Monitoring systems; Pest control; Plant diseases; Precision agriculture; Precision farming; Recall; Recommender systems; Regression; Sensors; SFSS MLR; Soil moisture; Soil temperature; Weathering; Precision agriculture; Internet of things; Accuracy; Machine learning; Sensors; Fertilizer quantity management; SFSS MLR
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-023-05484-8

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