A Quantized Deep Learning Model for Efficient Plant Leaf Disease Detection on Embedded Systems

You Only Look Once (YOLO) object detection network has gained significant adoption in the field of plant leaf disease detection due to its strong detection capabilities. However, deploying YOLO models on resource-constrained devices remains challenging, as they require substantial computational power. The complexity and size of these models pose significant obstacles for edge platforms, which are often limited in processing and memory resources. To address these limitations and accelerate inference, we propose a quantized version of YOLOv5x, called Quant-YOLOv5x. This quantization reduces the size and complexity of the model, making it more suitable for edge deployment while maintaining competitive detection accuracy. The experiments were carried out using a self-generated dataset focused on detecting tomato and pepper leaf diseases. Our quantization method reduces the bitwidth of the entire YOLO network to 8 bits, resulting in only a 2.8% decrease in mean Average Precision (mAP), a 50% reduction in model size, and an increase of 4.7 FPS compared to the standard YOLOv5 model.

@article{Tej2025,
  title     = {A Quantized Deep Learning Model for Efficient Plant Leaf Disease Detection on Embedded Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Balkis Tej and Soulef Bouaafia and Mohamed Ali Hajjaji and Abdellatif Mtibaa and Mohamed Atri},
  doi       = {10.14569/IJACSA.2025.0161193},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161193}
}