Research Article | Open Access

Volume 2022 |Article ID 9813841 | https://doi.org/10.34133/2022/9813841

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Qinlin Xiao,^1,2 Wentan Tang,^1,2 Chu Zhang,³ Lei Zhou,^1,2,4 Lei FengiD ,^1,2 Jianxun Shen,⁵ Tianying Yan,⁶ Pan Gao iD ,⁶ Yong He iD ,^1,2 Na Wu iD ^1,2

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
²Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
³School of Information Engineering, Huzhou University, Huzhou 313000, China
⁴College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
⁵Hangzhou Raw Seed Growing Farm, Hangzhou 311115, China
⁶College of Information Science and Technology, Shihezi University, Shihezi 832000, China

Abstract

Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.