IOT Enabled Weedicide Control Using Image Processing at Agriculture Field

Manjula, G. and Visu, P. and Chakaravarthi, S. (2021) IOT Enabled Weedicide Control Using Image Processing at Agriculture Field. EAI Endorsed Transactions on Smart Cities. e10. ISSN 2518-3893

[thumbnail of eai.30-6-2021.170252.pdf]
Available under License Creative Commons Attribution No Derivatives.

Download (3MB) | Preview


The Aim of this project is to automate plant monitoring and smart gardening using IOT in the Arduino Mega Platform. Identifying diseases in plants leave is a challenging task for farmers and also for researchers. The key highlight of the project is able to detect the type of disease by use of image processing. Image Processing steps are pre-processing, spot segmentation and features extraction, and classification. The extracted features are optimized by genetic algorithm and classified by KNN Classifier. We proposed a methodology that is tested for four types of apple plant disease including healthy leaves, Black Rot, Rust, and Scab. When the disease is identified we provided a pesticide solution displayed in the LCD Display and the same is sent to the farmer mobile with the help of GSM. All the Stages are monitored in an IOT Webpage.

Item Type: Article
Uncontrolled Keywords: IOT, Image Processing, Arduino Mega Platform, GSM, Smart farming
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 27 Jul 2021 13:49
Last Modified: 27 Jul 2021 13:49

Actions (login required)

View Item
View Item