Precision Farming with AI: An Integrated Deep Learning Solution for Paddy Leaf Disease Monitoring

Paddy rice, an essential food source for millions, is highly susceptible to various leaf diseases that threaten its yield and quality. This study introduces a cutting-edge hybrid deep learning model designed to address the critical need for accurate and timely identification and classification of paddy leaf diseases. Traditional methods often lack the precision and efficiency required for effective disease detection, necessitating the development of more sophisticated approaches. Our proposed model leverages the feature extraction capabilities of EfficientNetB0 and the hierarchical relationship capturing abilities of the Capsule Network, resulting in superior disease classification performance. The hybrid model demonstrates outstanding accuracy, achieving 97.86%, along with precision, recall, and F1-scores of 97.98%, 98.01%, and 97.99%, respectively. It effectively differentiates between diseases such as Narrow Brown Spot, Bacterial Leaf Blight, Leaf Blast, Leaf Scald, Brown Spot, and healthy leaves, showcasing its robustness in practical applications. This research highlights the importance of advanced technological interventions in agriculture, providing a scalable and efficient solution for disease detection in paddy crops. The hybrid deep learning model offers significant benefits to farmers and agricultural stakeholders, facilitating timely disease management, optimizing resource use, and improving crop management practices. Ultimately, this innovation supports agricultural sustainability and enhances global food security.

@article{K2024,
  title     = {Precision Farming with AI: An Integrated Deep Learning Solution for Paddy Leaf Disease Monitoring},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Pramod K and V. R. Nagarajan},
  doi       = {10.14569/IJACSA.2024.0150783},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150783}
}