Smart Agricultural Decision Support Systems for Predicting Soil Nutrition Value Using IoT and Ridge Regression

Abstract

Cost effective agricultural crop productivity is an everlasting demand, this predominant expedition has raised a global shift towards practicing smart agricultural methods to increase the productivity and the efficiency of the agricultural sector, using IoT. This research identified the benefits and the challenges in IoT adoption as an alternate for out-of-date agricultural practices. The proposed decision support system using IoT for Smart Soil Nutrition Prediction (SSNP) adopts IR sensors and implements diffuse reflectance infrared spectroscopy. Information is transferred using Arduino and Zigbee protocol. It has indicated precise outcomes in various studies giving a high repeatable, low cost and fast estimation of soil properties. The measure of light absorbed by a soil example is estimated, inside several particular wavebands over a scope of frequencies to yield an infrared range utilizing an IR sensor. Using the given values, the experimental analysis using the dataset and the nutrition values of the soil such as Ca, P, SOC, Sand and pH are predicted. This proposed IoT framework would enhance the farmer's knowledge regarding the type of crops they should grow to get maximum profit from their agricultural produce.