A comprehensive evaluation of broad learning system for deep feature- based chili leaf disease classification

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The early detection of plant leaf diseases is essential for the enhancement of crop productivity and the promotion of sustainable agricultural practices. While deep learning models have been shown to be achieve remarkable success in the recognition of plant disease, conventional classifiers commonly rely on iterative gradient-based optimization, resulting in increased training complexity. This present study investigates a hybrid framework for the classification of chili leaf disease that combines DenseNet201-based deep feature extraction with a Broad Learning System (BLS) classifier. The DenseNet201 model is employed to generate discriminative feature representations, whereas the BLS approach employs a closed-form ridge regression solution for classification. The present study involved experiments conducted by means of the publicly available Chili Plant Leaf Disease Dataset containing 1,856 original images from six different disease categories. To prevent data leakage, the dataset was initially partitioned into training, validation, and test subsets at the original-image level, with data augmentation being applied exclusively to the training set, thereby increasing it to 9,093 images. The proposed DenseNet201+BLS framework achieved a test accuracy of 99.28% and a macro F1-score of 99.00%. Furthermore, the performance of the proposed model was compared with that of Softmax, Logistic Regression, Random Forest, Multilayer Perceptron, and Support Vector Machine (SVM) classifiers using identical DenseNet201 feature representations. Among the evaluated classifiers, SVM demonstrated the highest level of accuracy (99.64%), whereas BLS exhibited a favorable balance between predictive performance and computational efficiency, requiring less than one second for training while outperforming Softmax, Logistic Regression, and Random Forest. Grad-CAM visualizations further demonstrated that the extracted deep features focus on disease-relevant regions such as lesions, discoloration patterns, and abnormal leaf structures. The findings indicate that the integration of DenseNet201 feature extraction with a Broad Learning System offers a competitive and computationally efficient alternative for the automated classification of chili leaf disease. The proposed framework facilitates accurate disease recognition with substantially reduced training costs, making it a promising solution for resource-efficient agricultural monitoring and decision-support applications.

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