Coffee Leaf Diseases Classification and the Effect of Fine-tuning on Deep Convolutional Neural Networks

Author(s)	Enoch Binney, Dongxiao Ren
Country	China
Abstract	This research proposes a method for the automatic diagnosis and classification of leaf diseases in Kenyan Arabica coffee leaves. We trained Deep Convolution Neural Learning models on the JMuBEN2 obtained from Mendeley data public access to determine whether a particular leaf image contains Phoma, Cercospora, or Rust. The proposed models for this work were the ResNet50, Densenet-121, and VGG19 architectures, all are well-known classification models. They were trained using transfer learning and fine-tuning and their respective outputs were compared based on these methods of training. After training the dataset using the aforementioned models, the Densenet-121 model was superior to the others gaining an accuracy of 95.44% after transfer learning and 99.36% after fine-tuning the model.
Keywords	Deep Learning, Convolutional Neural Network, Leaf Rust, Phoma, Cerscospora, Transfer Learning, Fine-Tuning
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-10-13
Cite This	Coffee Leaf Diseases Classification and the Effect of Fine-tuning on Deep Convolutional Neural Networks - Enoch Binney, Dongxiao Ren - IJFMR Volume 4, Issue 5, September-October 2022. DOI 10.36948/ijfmr.2022.v04i05.861
DOI	https://doi.org/10.36948/ijfmr.2022.v04i05.861
Short DOI	https://doi.org/grb58c

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