Spectroscopy and Spectral Analysis, Volume. 42, Issue 3, 788(2022)
Detecting Green Plants Based on Fluorescence Spectroscopy
[1] M Huang, Y Zheng, B Zhu Q et al. Computers and Electronics in Agriculture, 141, 215(2017).
[3] C Wang A, H Wei X, W Zhang. Computers and Electronics in Agriculture, 158, 226(2019).
[5] F Gao J, P Lootens, D Nuyttens et al. Biosystems Engineering, 170, 39(2018).
[6] S Bajwa, A Mireei S, A Shirzadifar et al. Biosystems Engineering, 171, 143(2018).
[8] T J C Amado, P Pott L, R A Schwalbert et al. Pest Management Science, 76, 1173(2020).
[9] Q Huang W, B Li J, Tian Xi et al. Computers and Electronics in Agriculture, 127, 582(2016).
[10] M Gabbouj, A Iosifidis, T Tran D. Pattern Recognition Letters, 100, 131(2017).
[11] D Bai X, G Cao Z, Y Wang et al. Biosystems Engineering, 125, 80(2014).
[12] H Huang, H Wang X, H Xu H et al. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 23, 787(2015).
[13] F Sun H, L Wang, H Zhang et al. Journal of Spectroscopy, 2018, 7652592(2018).
Get Citation
Copy Citation Text
Ai-chen WANG, Bin-jie GAO, Chun-jiang ZHAO, Yi-fei XU, Miao-lin WANG, Shu-gang YAN, Lin LI, Xin-hua WEI. Detecting Green Plants Based on Fluorescence Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2022, 42(3): 788
Category: Orginal Article
Received: Feb. 2, 2021
Accepted: Mar. 18, 2021
Published Online: Apr. 19, 2022
The Author Email: WANG Ai-chen (acwang@ujs.edu.cn)