@article{Sudha:320342,
      recid = {320342},
      author = {Sudha, Mohan Kumar  and Manorama, Maharana  and Aditi,  Tarigoppula },
      title = {Smart Agricultural Decision Support Systems for Predicting  Soil Nutrition Value Using IoT and Ridge Regression},
      journal = {AGRIS on-line Papers in Economics and Informatics},
      address = {2022-03-30},
      number = {665-2022-513},
      month = {Mar},
      year = {2022},
      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 This research  identified the benefits and the challenges in IoT adoption  as an alternate for out-of-date agricultural practices.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.},
      url = {http://ageconsearch.umn.edu/record/320342},
      doi = {https://doi.org/10.22004/ag.econ.320342},
}