Deep Learning-Driven Citrus Disease Detection: A Novel Approach with DeepOverlay L-UNet and VGG-RefineNet

Agriculture is essential to the world's desire to produce food, generate income, and maintain livelihoods. Citrus fruits are produced worldwide and have a significant impact on food production, nutrition, and agriculture. During production, farmers face difficulties due to diseases that affect plant growth. Black spot, canker, and greening are some citrus leaf diseases that risk citrus production, resulting in economic losses as well as reduced supply stability. Early detection of these diseases through recent technologies like deep learning will help farmers with better yields and quality. The current methods fall short in marking the area affected by the disease with accuracy and more performance. This work has a novel method proposed for the segmentation and classification of citrus leaf diseases. The method consists of three phases. In the first phase, DeepOverlay L-UNet is used to segment the affected regions. In the second phase, disease detection is carried out using VGG-RefineNet, and in the third phase, the affected region is highlighted in the original image with a severity level. On the other hand, the DeepOverlay L-UNet model proves to be effective in detecting affected areas, thereby enabling clear visualization of the spread of the disease. The result affirms that the proposed method outperforms with a better training IOU of 0.9864 and a validation IOU of 0.9334.

@article{Dinesh2024,
  title     = {Deep Learning-Driven Citrus Disease Detection: A Novel Approach with DeepOverlay L-UNet and VGG-RefineNet},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {P Dinesh and Ramanathan Lakshmanan},
  doi       = {10.14569/IJACSA.2024.01507100},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507100}
}