Color Features and KNN in Classification of Raw Arecanut images

Siddesha, S; Niranjan, S. K.; Aradhya, V. N. Manjunath

doi:10.1109/icgciot.2018.8753075

Cited by 9 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Siddesha et al. [10] proposes color features and KNN classifier for classification of raw arecanut images. Suresh et al.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the following methods are considered as relevant existing methods for comparative study. Siddesha et al [10] proposes color features and KNN classifier for classification of raw arecanut images. Suresh et al [6] uses texture features for classification of diseased peeled arecanut images.…”

Section: Resultsmentioning

confidence: 99%

“…For both the experiments, the proposed method achieves the highest classification rate, recall, precision, F-measure and accuracy compared to the existing methods. Siddesha et al [10] (color features) report lowest results compared to the proposed and other existing methods. The main reason for this weak performance for our dataset is that the method is not robust to handle the images affected by diseases.…”

Section: Experiments On Disease Identification Of Arecanut Imagesmentioning

confidence: 89%

See 2 more Smart Citations

Multi‐gradient‐direction based deep learning model for arecanut disease identification

Mallikarjuna

Shivakumara

Khare

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Arecanut disease identification is a challenging problem in the field of image processing. In this work, we present a new combination of multi‐gradient‐direction and deep convolutional neural networks for arecanut disease identification, namely, rot, split and rot‐split. Due to the effect of the disease, there are chances of losing vital details in the images. To enhance the fine details in the images affected by diseases, we explore multi‐Sobel directional masks for convolving with the input image, which results in enhanced images. The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding. Further, the features are extracted for foreground information for disease identification by exploring the ResNet architecture. The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images. Experimental results on the dataset of four classes (healthy, rot, split and rot‐split) show that the proposed model is superior in terms of classification rate.

show abstract

“…Siddesha et al. [10] proposes color features and KNN classifier for classification of raw arecanut images. Suresh et al.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Experiments On Disease Identification Of Arecanut Imagesmentioning

confidence: 89%

See 1 more Smart Citation

Multi‐gradient‐direction based deep learning model for arecanut disease identification

Mallikarjuna

Shivakumara

Khare

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…To make implementation easy and save processing time the input images size are converted to standard size of 256 × 256 dimension in the proposed work We consider the following most recently proposed approaches for comparative analysis to illustrate the robustness of the suggested method. Siddesh et al [10], which proposes color features and KNN classifier for classification of raw arecanut images. Suresh et al [6], which uses texture, features for classification of diseased arecanut images.…”

Section: Resultsmentioning

confidence: 99%

“…This shows that different types of arecanut image classification are at infant stage. Siddesh et al [10] proposed a method for classification of arecanut images using color features and KNN classifier. The method proposes the combination of color features and color moments and use histogram operation for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

CNN Based Method for Multi-Type Diseased Arecanut Image Classification

Mallikarjuna

Shivakumara

Khare

et al. 2021

MJCS

View full text Add to dashboard Cite

Arecanut image classification is a challenging task to the researchers and in this paper a new combined approach of multi-gradient images and deep convolutional neural networks for multi-type arecanut image classification is presented. To enhance the fine details in arecanut images affected by different diseases, namely, rot, split and rot-split, we propose to explore multiple-Sobel masks for convolving with the input image. Although, the images suffer from distortion due to disease infection, this masking operation helps to enhance the fine details. We believe that the fine details provide vital clues for classification of normal, rot, split and rot-split images. To extract such clues, we explore the combination of multi-gradient and AlexNet by feeding enhanced images as input. Implementation results on the four-class dataset indicate that the approach proposed is superior in terms of classification rate, recall, precision and F-measures. The same conclusion can be drawn from the results of comparative study of proposed method with the existing methods.

show abstract