Research Article
Deep Learning Based Pest Identification on Mobile
@INPROCEEDINGS{10.1007/978-3-030-49610-4_10, author={Yulin Duan and Dandan Li and Chongke Bi}, title={Deep Learning Based Pest Identification on Mobile}, proceedings={Smart Grid and Internet of Things. Third EAI International Conference, SGIoT 2019, TaiChung, Taiwan, December 5-6, 2019, Proceedings}, proceedings_a={SGIOT}, year={2020}, month={6}, keywords={Pest identification Deep learning Mobile}, doi={10.1007/978-3-030-49610-4_10} }- Yulin Duan
Dandan Li
Chongke Bi
Year: 2020
Deep Learning Based Pest Identification on Mobile
SGIOT
Springer
DOI: 10.1007/978-3-030-49610-4_10
Abstract
Crops, vegetables, fruit trees, flowers and other cash crops, are often harmed by a variety of harmful organisms, plant pathogens, pests, weeds and pest rats, etc. Plant diseases and insect pests often occur, which are one of the main factors which causes the damage of leaves and crop failure. Therefore, in order to stop the pest, it is extremely important to identify the pests of plants and their characteristics correctly. In this paper, an effective and scalable image recognition algorithm is proposed for disease detection. Meanwhile, MobileNets is employed for developing our method on mobile devices. Finally, a dataset consists of three apple diseases is used to demonstrate the effectiveness of our method. In the experiments, transfer learning is used to train a deep convolutional neural network for identifying two types of pest damage, apple rusts and apple Alternaria leaf spot. Our results show that the MobileNets model offer a fast, affordable, and easy-to-deploy strategy for plant disease detection.