Maize Disease Detection using Color Cooccurrence Features

Authors

  • Esmael Ahmed  Department of Information System, Wollo University, Dessie, Ethiopia
  • Kedir Abdu  Department of Information System, Wollo University, Dessie, Ethiopia

DOI:

https://doi.org//10.32628/CSEIT2390140

Keywords:

Plant Disease Detection; Color Co-occurrence Features

Abstract

The Ethiopian economy is based primarily on agriculture. Furthermore, due to Ethiopia's predominately agricultural economy, most Ethiopians are dependent on agriculture in some way. In Ethiopia, traditional dishes including bread, injera, and soup are commonly made from one of the plants, maize. Although growing maize, Wollo farmers experience low levels of yield due to a variety of problems. This study examines the features of color co-occurrence to identify Maize illness. Although it has not been proven, several diseases may occur in Ethiopia. In this research features from the images are retrieved, while the texture feature from the color co-occurrence matrix is used. Artificial Neural Networks and Leaf Color Analysis are used to categorize the diseases classified as Maize Blast, Brown Spot, Narrow Spot, and Normal Maize Leaf. Analyze and classify the Maize disease, the process entails acquiring, evaluating, and classifying images. The entire Maize sample goes through the leaf color analysis before moving on to the artificial neural network.. All samples are subjected to a leaf color analysis throughout the testing step in order to identify the leaf diseases. If the sample's RGB values fall within a predetermined range, it is automatically classified as a normal Maize leaf; nevertheless, all diseased samples undergo image processing in order to get the features that utilized to train and evaluate an artificial neural network. The generated model is then discovered when an artificial neural network is trained using these features. As a result, the artificial neural network technique is used to identify the Maize diseases with an accuracy rate of roughly 86%.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-03-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

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How to Cite

[1]
Esmael Ahmed, Kedir Abdu, " Maize Disease Detection using Color Cooccurrence Features, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.01-10, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2390140