An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Improving the quality and quantity of paddy production is very important since rice is the most consumed staple food for billion people around the world. Early detection of the paddy diseases and pests at different stages of growth is very crucial in paddy production. However, the current manual method in detecting and classifying the paddy diseases and pests requires a very knowledgeable farmer and time consuming. Thus, this study attempts to utilize an effective image processing and machine learning technique to detect and classify the paddy diseases and pests more accurately and less time processing. To accomplish this study, 3355 images comprises of 4 classes paddy images which are healthy, brown spot, leaf blast, and hispa was used. Then the proposed five layers of CNN technique is used to classify the images. The result shows that the proposed CNN technique is outperform and achieved the accuracy rate up to 93% as compared to other state-of-art comparative models.

@article{Senan2020,
  title     = {An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {7},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Norhalina Senan and Muhammad Aamir and Rosziati Ibrahim and N. S. A. M Taujuddin and W.H.N Wan Muda},
  doi       = {10.14569/IJACSA.2020.0110716},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110716}
}