Automated greenhouse system for tomato crop using deep learning. (13th August 2019)

• Record Type: Journal Article
• Authors: Dharwadkar, Nagaraj V.; Harale, Vandana R.
• Is Part Of: International journal of sustainable agricultural management and informatics. Volume 5:Number 2/3(2019)
• Page Start: 131
• Page End: 147
• Publication Date: 2019-08-13
• Subjects: automated greenhouse system -- climate variables -- DNN classifier -- tomato crop -- control system -- crop diseases; Sustainable agriculture -- Management -- Periodicals; Sustainable agriculture -- Data processing -- Periodicals; Agricultural productivity -- Management -- Periodicals; Agricultural productivity -- Data processing -- Periodicals; Agricultural resources -- Data processing -- Periodicals; Agricultural systems -- Management -- Periodicals; Agricultural systems -- Data processing -- Periodicals; 338.1
• Languages: English
• ISSNs: 2054-5819

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.