@Article{cmc.2020.012165,
AUTHOR = {Zongshuai Liu, Xuyu Xiang, Jiaohua Qin, Yun Tan, Qin Zhang, Neal N. Xiong},
TITLE = {Image Recognition of Citrus Diseases Based on Deep Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {457--466},
URL = {http://www.techscience.com/cmc/v66n1/40458},
ISSN = {1546-2226},
ABSTRACT = {In recent years, with the development of machine learning and deep
learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition
method of citrus diseases based on deep learning is proposed. We built a citrus
image dataset including six common citrus diseases. The deep learning network
is used to train and learn these images, which can effectively identify and classify
crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed, model
size, accuracy. Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy. Finally,
we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal, and put forward relevant suggestions.},
DOI = {10.32604/cmc.2020.012165}
}