FB-PNet: A Semantic Segmentation Model for Automated Plant Leaf and Disease Annotation

Semantic segmentation is an important operation in computer vision, which is generally plagued by computational resources and the time-consuming process for labor intensive of pixel-wise labeling. As a solution to this issue, the present study introduces a state-of-the-art segmentation system based on the Forward-Backward Propagated Percept Net (FB-PNet) architecture, augmented with Perception Convolution layers designed specifically for this purpose. The suggested method improves segmentation precision and processing the efficiency by capturing fine visual features and reducing some unnecessary data. The performance of the model is tested using key evaluation metrics, including Intersection over Union (IoU), Dice coefficient, Loss, Recall, and Precision. Experimental results indicate that the model works effective in segmenting leaf and disease regions in plant images without requiring full pixel-by-pixel labeling. Data augmentation techniques also greatly improve the capability of the model to handle new situations. A strong partitioning technique of the dataset allows for best performance testing, demonstrating the strength and flexibility of the model with respect to new data in the PlantVillage dataset, even without the employment of annotation masks. The innovation of this research is an efficient and scalable approach to large-scale plant leaf and disease detection, which is able to sustain precision agriculture application cases.

@article{Dinesh2025,
  title     = {FB-PNet: A Semantic Segmentation Model for Automated Plant Leaf and Disease Annotation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {P Dinesh and Ramanathan Lakshmanan},
  doi       = {10.14569/IJACSA.2025.0160549},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160549}
}