Development of a web based system to practice the estimation of plant disease severity
Daminda Thushara Andrahannadi, He Dong Jian, Wang Mei Li
grid method, image processing, leaf area, plant disease severity.
Identification of disease severity at the correct stage plays an important role in the maintenance of a healthy cultivation. Sometimes, farmers apply chemicals unnecessarily causing both health and environmental hazards. It is important to avoid such mal-practices by improving the awareness of farmers. Disease severity is the critical factor to take a decision on pesticide application. In most of the cases, farmers try to go for visual estimation by their own without the assistance of expertized officers. Accordingly, it is necessary to train farmers and other interested parties on estimation of the disease severity. this research aims to develop a web site to allow stakeholders to practice and improve their skills on estimation of disease severity. In this research image processing techniques were applied for the calculation of leaf area and then calculated the disease severity using these estimates. Increased availability of cameras and other devices with embedded cameras thrive the adaptability of digital image processing. Digital images of infected leaf samples were input to the system and open source software image j was used to calculate the total leaf area and infected area. Disease severity was estimated using these values and they were stored in a database. Data base was created with MySQL and it was link with the website using php. Web site was created using html, php and Java scripts. Login accounts were created for individual users and they were allowed to logon to the system using their accounts. Then, they can practice the estimation of diseases using the sample images available in the web site. First, user has to feed the estimated value according to him and then the system will provide the accurate value of disease severity. Users are facilitated to ask any number of samples according to their desire. Accuracy of the algorithm was tested by comparing the obtained values with the grid count method. Accordingly, average accuracy of the estimation of disease severity is 96.12% and it is better than the traditional approach since it is faster and machine oriented.
Article Details
Unique Paper ID: 149117

Publication Volume & Issue: Volume 6, Issue 11

Page(s): 30 - 36
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