Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

  • Savitha P
  • Rai A
  • Singh N
  • et al.
N/ACitations
Citations of this article
8Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Global climatic changes in the recent era have resulted in a significant global food shortage. Where there is a vast population and a diverse agricultural sector, food security is a major problem. Growing competition for land, water, and energy will undermine our capital food production, and the effect of climate change will continue to be a challenge. The integrated greenhouse and warehouse provide quality and sustainable food production. In this paper an integrated green house with warehouse monitoring hardware implementation, with novel feature of customized disease detection with convolution neural network (CNN) algorithm is presented. The real time monitoring of data of different sensors are displayed on the screen/ dashboard developed by the android application. This aids farmers in making wise choices on crop management and disease detection of greenhouse crops and effective monitoring.

Cite

CITATION STYLE

APA

Savitha, P. B., Rai, A., Singh, N., Keshav, C. C., & Neelambike, V. (2023). Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning. IOP Conference Series: Materials Science and Engineering, 1295(1), 012010. https://doi.org/10.1088/1757-899x/1295/1/012010

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free