Convolutional Neural Network for Chili Plant Disease Classification: A Deep Learning Approach

Chili peppers are a high-value horticultural crop that is highly susceptible to foliar diseases, which can significantly reduce yield and market quality. This study proposes and evaluates a Convolutional Neural Network (CNN) model based on the MobileNet-V2 architecture for chili leaf disease classification. A combined dataset consisting of 2,690 images collected from two public repositories and one field-acquired source was used in this research. The dataset was divided into training, validation, and testing subsets using an 80:10:10 ratio and underwent preprocessing steps including image resizing, data augmentation, and normalization. The proposed model was implemented using TensorFlow 2.15 and trained on the Google Colab platform. Experimental results demonstrate strong classification performance, achieving 95.6% validation accuracy and 96.8% test accuracy with a low loss value of 0.1011. All evaluated classes, anthracnose, yellow virus, leaf spot, leaf curl, and healthy leaves achieved precision, recall, and F1-scores exceeding 0.90, accompanied by near-perfect AUC values. These findings indicate that the MobileNet-V2-based CNN exhibits effective discriminative capability and generalization across heterogeneous visual conditions, highlighting its potential applicability for AI-assisted agricultural disease monitoring systems based on image processing techniques.

@article{Astuti2026,
  title     = {Convolutional Neural Network for Chili Plant Disease Classification: A Deep Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Erna Dwi Astuti and Widowati and Aris Sugiharto},
  doi       = {10.14569/IJACSA.2026.0170354},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170354}
}