Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 179, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105828
Keywords
Diseases recognition; Zero-shot learning; Citrus aurantium L.; Adversarial autoencoders
Funding
- National Natural Science Foundation of China [62006035, 62002044]
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Plant diseases can cause significant production and economic losses, and also seriously restrict the sustainable development of agriculture. Traditional plant diseases recognition method is time-consuming and highly dependent on expert experience. Therefore, most of the existing works design models based on deep learning to automatic recognition. However, they are sample-intensive and hard for the diagnosis of some Citrus aurantium L. diseases with only a few or even zero labeled samples for training. In this paper, we propose a novel generative model for zeroand few-shot recognition of Citrus aurantium L. diseases using conditional adversarial autoencoders (CAAE). CAAE learns to synthesize visual features so that the zeroand few-shot recognition can be transformed to a conventional supervised classification problem. Specifically, CAAE consists of encoder, decoder, and discriminator. Different from conditional variational autoencoder (CVAE), we impose a discriminator to train the encoder by adversarially minimizing the loss between the prior distribution and the encoding distribution. Our model achieves a harmonic mean accuracy of 53.4% for zero-shot recognition of Citrus aurantium L. diseases, which is 50.4% higher than CVAE. Extensive experiments carried out on public zero-shot benchmark datasets and a further case study on our own collected dataset of Citrus aurantium L. diseases demonstrate that our model is suitable for the application of zeroand few-shot Citrus aurantium L. diseases diagnosis.
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