An Efficient Ensemble Feature Selection-Based Segmentation and Multi-Class Classification for Heterogeneous Plant Disease and Non-Disease Images

Abstract

In recent years, the rapid advancement of computer vision and machine learning techniques has revolutionized the field of agriculture by enabling automated detection and classification of plant diseases. This work presents a comprehensive approach for the efficient segmentation and multi-class classification of heterogeneous plant disease and non-disease images. The proposed methodology combines multi-level thresholding-based filtering, feature ranking, leaf feature segmentation, and ensemble classification to achieve high accuracy in disease recognition. In the initial stage, multi-level thresholding-based filtering is applied to enhance image quality and reduce noise, facilitating more precise disease pattern extraction. Subsequently, feature ranking methods are employed to select the most discriminative features from the pre-processed images. These features play a pivotal role in distinguishing between various plant diseases and healthy leaves. Experimental results on a multi-modal plant disease and non- disease images demonstrate the superiority of our proposed method in terms of accuracy and efficiency.

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