Estimation of Arecanut Yield in Various Climatic Zones of Karnataka using Data Mining Technique: A Survey

Manjunatha, M. J.; Parkavi, A

doi:10.1109/icctct.2018.8551083

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Sample arecanut images of the four classes can be seen in Figure 1. There are methods for classifying different types of arecanut images affected by the disease in the literature [4][5][6] but most of the methods use geometrical features, shape-based features, texture-based features of the images and conventional classifiers, such as SVM for classification. As a result, these methods are not robust to variations in the image affected by multiple diseases.…”

Section: Rot Split Diseasementioning

confidence: 99%

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

Mallikarjuna

Shivakumara

Khare

et al. 2022

CAAI Trans on Intel Tech

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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.

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Section: Rot Split Diseasementioning

confidence: 99%

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

“…There are methods for classifying different type of arecanut images including affected by disease in the literature [4,5,6] but most of the methods use geometrical features, shape based features, texture based features of the images and conventional classifier, such as SVM for classification. As a result, these methods are not robust for variation in the image affected by multiple diseases.…”

Section: Rot Split Diseasementioning

confidence: 99%

CNN Based Method for Multi-Type Diseased Arecanut Image Classification

Mallikarjuna

Shivakumara

Khare

et al. 2021

MJCS

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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.

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Comparative analysis of machine learning techniques for predicting production capability of crop yield

Jain¹,

Choudhary²

2022

Int J Syst Assur Eng Manag

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Estimation of Arecanut Yield in Various Climatic Zones of Karnataka using Data Mining Technique: A Survey

Cited by 5 publications

References 14 publications

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

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

CNN Based Method for Multi-Type Diseased Arecanut Image Classification

Comparative analysis of machine learning techniques for predicting production capability of crop yield

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