Can Deep Learning Identify Tomato Leaf Disease?

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ID: 7766
2018
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Abstract
This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.
Reference Key
keke2018canadvances Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Keke;Wu, Qiufeng;Liu, Anwang;Meng, Xiangyan;Zhang, Keke;Wu, Qiufeng;Liu, Anwang;Meng, Xiangyan;
Journal advances in multimedia
Year 2018
DOI
10.1155/2018/6710865
URL
https://www.hindawi.com/journals/am/2018/6710865/abs/
Keywords Keywords not found

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