Title: Automated greenhouse system for tomato crop using deep learning

Authors: Nagaraj V. Dharwadkar; Vandana R. Harale

Addresses: Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Rajaramnagar, Islampur-415414, India Affiliated to: Shivaji University, India ' Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Rajaramnagar, Islampur-415414, India Affiliated to: Shivaji University, India

Abstract: In India, many farmers are facing the major problem of crop diseases. These diseases are affecting growth and the quality of the crop. The crop diseases will occur due to change in the environment variables. To reduce the impact of diseases, the farmers required to continuously monitor the health of the crop. The only solution to this problem is to monitor the health of the crop using an automated greenhouse system. In this paper, we have proposed an automated greenhouse system for tomato crop, considering the six climate variables like temperature, air humidity, soil moisture, pH value, CO2, light intensity. The proposed system uses deep neural network model for recognition of change in environmental variables. The results show that the deep neural network (DNN) model is able to reach the accuracy 90% in recognition of change in environment. By monitoring environmental facts, we can able to reduce the impact of diseases and improve the quality of the tomato crop.

Keywords: automated greenhouse system; climate variables; DNN classifier; tomato crop; control system; crop diseases.

DOI: 10.1504/IJSAMI.2019.101674

International Journal of Sustainable Agricultural Management and Informatics, 2019 Vol.5 No.2/3, pp.131 - 147

Received: 26 Nov 2018
Accepted: 07 Apr 2019

Published online: 13 Aug 2019 *

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