Misdiagnosis of the many diseases impacting agricultural crops can lead to misuse of chemicals leading to the emergence of resistant pathogen strains, increased input costs, and more outbreaks with significant economic loss and environmental impacts. Current disease diagnosis based on human scouting is time-consuming and expensive, and although computer-vision based models have the promise to increase efficiency, the great variance in symptoms due to age of infected tissues, genetic variations, and light conditions within trees decreases the accuracy of detection.

https://www.kaggle.com/c/plant-pathology-2020-fgvc7/overview

• For this problem fastai library is used which is based on the top of pytorch

• For this problem several architectures are used
  

  • Resnet
    
  • Efficient-net
    

• Along with this techniques such as
  

  • Data Augmentaion
    
  • Mixup Augmentation
    
  • Learning Rate Anealing
    
  • Label Smoothing
    are used for improving the model accuracy
    

• The Best accuracy scored is 0.95856