Automated classification of diseased cauliflower:
a feature-driven machine learning approach

Automated classification of diseased cauliflower:
a feature-driven machine learning approach

Analysis of variance
Bagging classifier
Cauliflower
Image processing
Machine learning

Cauliflower is a popular winter crop in Bangladesh. However, cauliflower plants are vulnerable to several diseases that can reduce the cauliflowers’ productivity and degrade their quality. The manual monitoring of these diseases takes a lot of effort and time. Therefore, automatic classification of the diseased cauliflower through computer vision techniques is essential. This study has retrieved ten different statistical and gray-level co-occurrence matrix (GLCM)-based features from the cauliflower image dataset by implementing a variety of image processing techniques. Afterwards, the SelectKBest method with the analysis of variance f-value (ANOVA F-value) has been used to identify the most important attributes for classification of the diseased cauliflower. Based on the ANOVA F-value, the top N (5≤N ≤9) most dominant attributes is used to train and test five machine learning (ML) models for classification of diseased cauliflower. Finally, different performance metrics have been used for evaluating the effectiveness of the employed ML models. The bagging classifier achieved the highest accuracy of 82.35%. Moreover, this model has outperformed other ML classifiers in terms of other performance metrics also.

Mala Rani Barman1, Al Amin Biswas2, Marjia Sultana3, Aditya Rajbongshi4, Md. Sabab Zulfiker2, Tasnim Tabassum5

Journal homepage: http://telkomnika.uad.ac.id

Dec 27, 2023

PERI IRAWAN

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