Cost-effective identification of the field maturity of tobacco leaves based on deep semi-supervised active learning and smartphone photograph

Author:

Sun TongORCID,Xue Chaoqun,Chen Yang,Zhao Luyao,Qiao Chenchen,Huang Aoyu,Li Feitao,Liu Chao,Wang Heng,Yang Yongfeng,Yao Qian,Wang Gaojie,Sun Chengzhong,Tian Tao,Qiao HongboORCID

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference38 articles.

1. Abuduweili, A., Li, X., Shi, H., Xu, C.Z., Dou, D., 2021. Adaptive Consistency Regularization for Semi-Supervised Transfer Learning, Computer Vision and Pattern Recognition.

2. A modified random forest approach to improve multi-class classification performance of tobacco leaf grades coupled with NIR spectroscopy;Bin;RSC Adv.,2016

3. A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis;Budd;Med. Image Anal.,2019

4. Study on the quantitative relationship between maturity and quality of tobacco leaf;Cai;Acta Tabacaria Sinica,2005

5. The effect of irrigation scheduling and water stress on the maturity and chemical composition of Virginia tobacco leaf;Cakir;Field Crop Res,2010

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