Visible/NIR Spectral Inversion of Malondialdehyde Content in JUNCAO Based on Deep Convolutional Gengrative Adversarial Network

ObjectiveJUNCAO, a perennial herbaceous plant that can be used as medium for cultivating edible and medicinal fungi. It has important value for promotion, but the problem of overwintering needs to be overcome when planting in the temperate zone. Low-temperature stress can adversely impact the growth of JUNCAO plants. Malondialdehyde (MDA) is a degradation product of polyunsaturated fatty acid peroxides, which can serve as a useful diagnostic indicator for studying plant growth dynamics. Because the more severe the damage caused by low temperature stress on plants, the higher their MDA content. Therefore, the detection of MDA content can provide instruct for low-temperature stress diagnosis and JUNCAO plants breeding. With the development of optical sensors and machine learning technologies, visible/near-infrared spectroscopy technology combined with algorithmic models has great potential in rapid, non-destructive and high-throughput inversion of MDA content and evaluation of JUNCAO growth dynamics.MethodsIn this research, six varieties of JUNCAO plants were selected as experimental subjects. They were divided into a control group planted at ambient temperature (28°C) and a stress group planted at low temperature (4°C). The hyperspectral reflectances of JUNCAO seedling leaves during the seedling stage were collected using an ASD spectroradiomete and a near-infrared spectrometer, and then the leaf physiological indicators were measured to obtain leaf MDA content. Machine learning methods were used to establish the MDA content inversion models based on the collected spectral reflectance data. To enhance the prediction accuracy of the model, an improved one-dimensional deep convolutional generative adversarial network (DCAGN ) was proposed to increase the sample size of the training set. Firstly, the original samples were divided into a training set (96 samples) and a prediction set (48 samples) using the Kennard stone (KS) algorithm at a ratio of 2:1. Secondly, the 96 training set samples were generated through the DCGAN model, resulting in a total of 384 pseudo samples that were 4 times larger than the training set. The pseudo samples were randomly shuffled and sequentially added to the training set to form an enhanced modeling set. Finally, the MDA quantitative detection models were established based on random forest (RF), partial least squares regression (PLSR), and convolutional neural network (CNN) algorithms. By comparing the prediction accuracies of the three models after increasing the sample size of the training set, the best MDA regression detection model of JUNCAO was obtained.Results and Discussions(1) The MDA content of the six varieties of JUNCAO plants ranged from 12.1988 to 36.7918 nmol/g. Notably, the MDA content of JUNCAO under low-temperature stress was remarkably increased compared to the control group with significant differences (P