Swift and efficient cinnamon plant disease classification using robust feature extraction and machine learning techniques
Sujithra Thandapani, Durai Selvaraj, Mohamed Iqbal Mahaboob, Venkatakrishnan Chakravarathi Bharathi, Prabhu Vinayagam
Abstract
The extraction of features and textures holds a crucial significance in the realm of image processing and machine vision systems. Even though artificial intelligence (AI) techniques are superior in attaining the best results in image processing, several challenges remain open for further research in computation complexity, memory, and power requirements. In this context, robust preprocessing techniques are required to address such shortcomings and reduce the computational cost of predictive tasks. This paper employed two feature extraction levels to extract the best possible features from images of the cinnamon plant. Local directional positional pattern (LDPP) extracts global image features, while local triangular coded pattern (LTCP) extracts local features. It helps to provide detailed and more relevant information about the image texture. Once features are extracted, identifying and categorizing diverse textures within an image relies on recognizing their unique features. Typically, descriptors serve as the means for representing images in our work. Afterwards, we used ensemble learning to attain better classification results with the help of weak classifiers. Extracted features are provided to machine learning (ML) models like support vector machines (SVM), random forest (RF), and k-nearest neighbors (KNN) for better classification of the cinnamon category.
Keywords
Cinnamon; Ensemble learning; Feature/texture extraction; K-nearest neighbors; Local directional positional pattern; Local triangular coded pattern
DOI:
https://doi.org/10.11591/eei.v14i1.8254
Refbacks
There are currently no refbacks.
This work is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License .
<div class="statcounter"><a title="hit counter" href="http://statcounter.com/free-hit-counter/" target="_blank"><img class="statcounter" src="http://c.statcounter.com/10241695/0/5a758c6a/0/" alt="hit counter"></a></div>
Bulletin of EEI Stats
Bulletin of Electrical Engineering and Informatics (BEEI) ISSN: 2089-3191, e-ISSN: 2302-9285 This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) .