Rapidly Detecting Chlorophyll Content in Oilseed Rape Based on Spectral Reconstruction and Its Device Development
WENG Hai-yong1, HUANG Jun-kun1, WAN Liang2, YE Da-peng1*
1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2. College of Biological System Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Abstract In order to rapidly and nondestructively detect chlorophyll content in leaves, a handheld multi-spectral imaging system was developed in this study to collect multispectral images of oilseed rape leaves. The pseudo-inverse method was introduced to reconstruct the multispectral reflectance at 6 wavebands (460, 520, 660, 740, 840 and 940 nm) to the hyperspectral reflectance at 512 wavebands in the range of 379～1 023 nm with the aim to improve the spectral resolution. The partial least square regression (PLSR) was then used to build a model to predict chlorophyll content in leaves based on the reconstructed hyperspectral reflectance. The results showed that the reflectance in the visible range of the reconstructed hyperspectral presented a high relationship with the chlorophyll content. The performance of PLSR model using reconstructed spectrum as inputs was evaluated using the parameters of the determination coefficient of prediction set (R2p), root mean square error of prediction (RMSEP) and residual prediction deviation (RPD) with the values of 0.78, 1.50 and 2.14, respectively, which was better than that using original spectrum at 4 wavebands (460, 520, 660 and 740 nm) with the values of R2p, RMESP and RPD of 0.72，1.85 and 1.88, respectively. The results demonstrated that the combination multispectral imaging with spectral reconstruction technology could improve the predicting ability of the PLSR model and this technology can be used for monitoring physiology and nutrient status in oilseed rape leaves.
[1] LEI Xiang-xiang, ZHAO Jing, LIU Hou-cheng, et al(雷祥祥, 赵 静, 刘厚诚, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(10): 3256.
[2] SUN Hong, ZHAO Yi, ZHANG Meng, et al(孙 红, 赵 毅, 张 猛，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, (S2): 186.
[3] TAN Wen-jing, WANG Yong-qian, ZHAO Peng-fei, et al(谭文静，王永千，赵鹏飞，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014，30(10)：160.
[4] YANG Biao, DU Rong-yu, YANG Yu, et al(杨 彪, 杜荣宇, 杨 玉，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019，50(12)：180.
[5] SUN Hong, XING Zi-zheng, ZHANG Zhi-yong, et al(孙 红, 邢子正, 张智勇，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2019, 50(S1): 175，296.
[6] XIE Jing, CHEN Shi, WANG Jun-ke, et al(谢 静，陈 适，王珺珂，等). Journal of Central China Normal University·Natural Science(华中师范大学学报·自然科学版)，2014，(2)：269.
[7] Ling Qihua, Huang Weihua, Jarvis Paul. Photosynthesis Research, 2011, 107(2): 209.
[8] SHI Ji-yong, LI Wen-ting, GUO Zhi-ming, et al(石吉勇, 李文亭, 郭志明，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50: 264.
[9] HONG Tian-sheng, GAN Hai-ming, YUE Xue-jun, et al(洪添胜, 甘海明, 岳学军，等). Journal of South China Agricultural University(华南农业大学学报), 2018, 39: 102.
[10] Feng Hui, Chen Guoxing, Xiong Lizhong, et al. Frontiers in Plant Science，2017, 8: 1238.
[11] Shi J Y, Zou X T, Zhao J W, et al. Scientia Horticulturae，2012, 138(1): 190.
[12] Li Dong, Cheng Tao, Jia Min, et al. Remote Sensing of Environment，2018, 206: 1.
[13] Yoon S C, Shin T S, Heitschmidt G W, et al. Journal of Biosystems Engineering，2019, 44(3): 169.
[14] CHEN Li-dan, ZHAO Yan-ru(陈立旦, 赵艳茹). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(8): 168.
[15] LIU Jiao, LI Xiao-yu, GUO Xiao-xu, et al(刘 娇, 李小昱, 郭小许，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30: 276.
