Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

TL;DR

This paper presents a deep learning and machine learning approach combining feature extraction, selection, and SVM classification to accurately identify potato leaf diseases, achieving up to 99% accuracy.

Contribution

It introduces a novel combination of deep CNN feature extraction, wrapper-based feature selection, and SVM classification for potato disease detection.

Findings

Achieved 99% accuracy with SVM classifier.

Selected 550 features for optimal performance.

Demonstrated effectiveness of combined feature selection and deep learning.

Abstract

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features.