Research Article
Artificial Intelligence Based Image Processing Model for Early Pest Detection in Sorghum Crop Plantations: In Case of Southern Tigrai
Asfaw Kiros Desalegn*
,
Kahsay Niguse Gebrekidan
Issue:
Volume 1, Issue 2, December 2026
Pages:
69-79
Received:
15 July 2026
Accepted:
15 September 2026
Published:
15 October 2026
DOI:
10.11648/j.sdcomput.20260102.11
Downloads:
Views:
Abstract: Agriculture not only provides food for the human existence, it is also a big source for the economy of any country. Pests damage the crops and, thus, are very dangerous for the overall growth of the crop. In this proposed system sorghum leaf image was given as an input and for noise removal median filtering algorithm is used then the background subtraction takes place using Otsu thresholding algorithm. Then, at the end of image processing step extract and calculate three types of feature color, texture and morphological features for the segmented foreground leaf image. After feature selection algorithm is implemented, six color, five texture and ten morphological features are extracted from each sorghum plant leaf image and combined to get dataset which is used to develop the model, though model attempts with separate features were conducted in experiment. Finally, these extracted features are feed in to the classifier for the classification, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network implemented for classification purposes. In general the overall experimental result shows that the developed Artificial Neural Network model with a combination of three features namely color, texture and morphological features achieved a better result with an accuracy of 90%.
Abstract: Agriculture not only provides food for the human existence, it is also a big source for the economy of any country. Pests damage the crops and, thus, are very dangerous for the overall growth of the crop. In this proposed system sorghum leaf image was given as an input and for noise removal median filtering algorithm is used then the background sub...
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