Xiao Qinlin, Tang Wentan, Zhang Chu, Zhou Lei, Feng Lei, Shen Jianxun, Yan Tianying, Gao Pan, He Yong, Wu Na
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
Plant Phenomics. 2022 Aug 16;2022:9813841. doi: 10.34133/2022/9813841. eCollection 2022.
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.
快速测定叶绿素含量对于评估棉花的营养和生理状况具有重要意义。配备多变量分析方法的高光谱技术已广泛用于叶绿素含量检测。然而，由于样本和测量条件等差异，基于一批样本或品种建立的模型对另一批样本或品种无法产生相同的效果。考虑到为每个批次或品种建立模型成本高昂，本文探索了使用光谱预处理结合深度迁移学习进行模型迁移的可行性。讨论了七种不同的光谱预处理方法，并开发了一个自行设计的卷积神经网络（CNN），通过微调来建立模型并执行迁移任务。一阶导数（FD）和标准正态变量变换（SNV）相结合的方法被选为最佳预处理方法。对于目标域数据集，基于FD + SNV处理后的光谱进行微调的CNN优于传统的偏最小二乘法（PLS）和平方支持向量机回归（SVR）。虽然在较小数据集上微调的CNN性能略低，但仍优于传统模型并取得了满意的结果。综合预处理结合深度迁移学习可能是估计不同棉花品种间叶绿素含量的有效方法，为田间评估棉花营养状况提供了新的可能性。
