Rice leaf disease classification using resource-efficient deep learning models via response-based knowledge distillation

This publication has 18 references indexed in Scilit:

• Edge AI-enabled chicken health detection based on enhanced FCOS-Lite and knowledge distillation
  Computers and Electronics in Agriculture, 2024
• Development of deep and machine learning convolutional networks of variable spatial resolution for automatic detection of leaf blast disease of rice
  Computers and Electronics in Agriculture, 2024
• Resource-efficient federated learning over IoAT for rice leaf disease classification
  Computers and Electronics in Agriculture, 2024
• A review of convolutional neural networks in computer vision
  Artificial Intelligence Review, 2024
• Knowledge distillation approach towards melanoma detection
  Computers in Biology and Medicine, 2022
• Knowledge distillation in plant disease recognition
  Neural Computing and Applications, 2022
• An industrial-grade solution for agricultural image classification tasks
  Computers and Electronics in Agriculture, 2021
• Image recognition of four rice leaf diseases based on deep learning and support vector machine
  Computers and Electronics in Agriculture, 2020
• Deep feature based rice leaf disease identification using support vector machine
  Computers and Electronics in Agriculture, 2020
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
  International Journal of Computer Vision, 2019