Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1472
Title: Intelligent Disease Detection System for Greenhouse with a Robotic Monitoring System
Authors: Fernando, S.
Nethmi, R.
Silva, A.
Perera, A.
De Silva, R.
Abeygunawardhana, P.K.W.
Keywords: Greenhouses
Disease diagnosis
Image processing
Machine Learning
Deep Learning
Tomato Farming
Issue Date: 10-Dec-2020
Publisher: 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT
Series/Report no.: Vol.1;
Abstract: Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attentiongrabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.
URI: 978-1-7281-8412-8
http://rda.sliit.lk/handle/123456789/1472
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020
Department of Computer Systems Engineering-Scopes

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