Classifying Crop Leaf Diseases using Different Deep Learning Models with Transfer Learning

Indexed incrossref

Abstract

Within the scope of the research, we put forward a technique of exactly confirming the distinctiveness of agricultural leaf pathologies with the assist of deep mastering algorithms and switch getting to know generation. We have pre-skilled models like VGG19, MobileNet, InceptionV3, EfficientNetB0, Simple CNN where we are seeking to increase the utility for the crop disorder type. Through searching at some metrics as cited Accuracy, Precision, Recall and F1 score for a better knowledge of a crop leaf photo category, we observe how each version performs. Our paper shows that artificial intelligence is fairly useful for the obligations of the automatic disease detection and switch mastering (as a method for…

Citation impact

960
total citations
FWCI
516.93
Percentile
100%
References
26
Citations per year

Authors

3

Topics & keywords

Keywords
  • Interpretability
  • Computer science
  • Artificial intelligence
  • Optimal distinctiveness theory
  • Scalability
  • Machine learning
  • Deep learning
  • Transfer of learning
UN Sustainable Development Goals
  • Zero hunger
No related works found for this paper.