Prickly Pear Disease Classification Using Deep Convolutional Neural Networks: A Case Study

Prickly pear (Opuntia ficus-indica) is a member of the Cactaceae family. Because of its anti-inflammatory, anti-oxidant, antibacterial, hypoglycemic, and neuroprotective properties, prickly pears are a magical fruit. Both the fruit and its stem are utilized in value-added products. Deep learning (DL) applications are required for prickly pear disease detection and classification. To the best of our knowledge, no previous study has investigated prickly pear disease classification using convolutional neural networks. In this study, we propose the use of deep convolutional neural networks MobileNetV2 and DenseNet121, to classify prickly pear disease. A locally collected dataset from Tunisia was divided into two classes: healthy and cochineal. Data augmentation techniques were applied to increase the number of images. These augmented data were then fed as input into MobileNetV2 and DentNet121 networks. The experimental results show that MobileNetV2 achieved a precision, recall, and F1-score of 96.55% for healthy plants. For diseased plants, precision, recall, and F1-score reached 97.14%. Overall, the model obtained a classification accuracy of 96.88%. DenseNet121 achieved precision, recall, and F1-score values of 90.62%, 100%, and 95.08%, respectively, for healthy plants. For diseased plants, the precision, recall, and F1-score were 100%, 91.43%, and 95.52%, respectively, resulting in an overall classification accuracy of 95.31%. Our proposed deep learning models, MobileNetV2 and DenseNet121, perform well and demonstrate strong performance on the prickly pear dataset.

@article{Elghawth2026,
  title     = {Prickly Pear Disease Classification Using Deep Convolutional Neural Networks: A Case Study},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Raghiya Elghawth and Wafae Abbaoui and Soumia Ziti},
  doi       = {10.14569/IJACSA.2026.0170334},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170334}
}