An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset

Due to its tasty and spicy fruit with nutritional qualities, chili is a demanding crop widely farmed around the world. Hence, it is essential to accurately determine the health status of chili for agricultural productivity. Recent years have seen impressive results in recognition fields due to deep learning approaches. However, deep learning models’ networks need an abundant data to perform well and collecting enormous data for the networks is time-consuming and resource-intensive. A data augmentation method is proposed to overcome this problem. It was applied to a small dataset of healthy and diseased chili leaf by utilizing geometric transformation method. Eventually, two deep learning models of CNN and ResNet-18 were evaluated using augmented and original datasets. From a series of experiment, it can be concluded that the trained deep learning models using original and augmented datasets perform better with an average accuracy performance of 97%.

@article{Aminuddin2022,
  title     = {An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Nuramin Fitri Aminuddin and Zarina Tukiran and Ariffuddin Joret and Razali Tomari and Marlia Morsin},
  doi       = {10.14569/IJACSA.2022.0130750},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130750}
}