Crop Yield Prediction Using Deep Neural Network

Fatin Farhan Haque, Ahmed Abdelgawad, Venkata Prasanth Yanambaka, Kumar Yelamarthi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Agriculture has made it's way to make every living being healthy and survive in this world, for which the environment affecting has been taken into consideration. The parameters that have impacted on the crops significant yield water, ultraviolet (UV), pesticides, fertilizer, and the area of the land covered for the region is referenced. In this paper, a machine learning model proposed illustrated the use of neural network and the concerned algorithm artificial neural network (ANN) has been evaluated. The dataset has been taken of 140 data points depicting the attributes effect on the yield of the crops. The error rate with the actual has been shown with the assist of Mean Square Error (MSE) and the standard deviation between the yield results with the actual was also shown, which came out to be 0.0045 for the MSE, that's around and 0.000345 as the standard deviation.

Original languageEnglish
Title of host publicationIEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155036
DOIs
StatePublished - Jun 2020
Event6th IEEE World Forum on Internet of Things, WF-IoT 2020 - New Orleans, United States
Duration: Jun 2 2020Jun 16 2020

Publication series

NameIEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings

Conference

Conference6th IEEE World Forum on Internet of Things, WF-IoT 2020
Country/TerritoryUnited States
CityNew Orleans
Period06/2/2006/16/20

Keywords

  • ANN
  • Crop
  • MSE
  • Machine Learning
  • Unsupervised

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