Image Features Based Intelligent Apple Disease Prediction System: Machine Learning Based Apple Disease Prediction System
Mahvish Jan and
Additional contact information
Mahvish Jan: Central University of Jammu, Jammu, India
Hazik Ahmad: Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India
International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2020, vol. 11, issue 3, 31-47
A pattern classifier (PC) is used to solve a variety of non-separable and complex computing problems. One of the key problems is to efficiently predict a type of disease in a typical fruit tree. The timely and accurately predicted disease in an apple tree may help a farmer to take appropriate preventive measures in advance. In this article, an apple disease diagnosis system is developed to predict the apple scab and leaf/spot blight diseases. In this article, low level and shape-based features are used for the development of an intelligent apple disease prediction system. First, the key image features like entropy, energy, inverse difference moment (IDM), mean, standard deviation (SD), perimeter, etc., are extracted from the apple leaf images. The model for the proposed system is trained by using multi-layer perceptron (MLP) pattern classifier and eleven apple leaves image features. The Gradient descent back-propagation algorithm is used for building the intelligent system to carry out the pattern classification. The proposed system is tested using some random samples and exhibits excellent diagnosis accuracy of 99.1%. The sensitivity of the proposed prediction model is 98.1% and specificity of ~99.9%.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJAEIS.2020070103 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:11:y:2020:i:3:p:31-47
Access Statistics for this article
International Journal of Agricultural and Environmental Information Systems (IJAEIS) is currently edited by Frederic Andres
More articles in International Journal of Agricultural and Environmental Information Systems (IJAEIS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().