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Smart Agriculture ›› 2023, Vol. 5 ›› Issue (3): 132-141.doi: 10.12133/j.smartag.SA202307011

• Special Issue--Monitoring Technology of Crop Information • Previous Articles     Next Articles

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

YE Dapeng1,2(), CHEN Chen1,2, LI Huilin1,2, LEI Yingxiao3, WENG Haiyong1,2, QU Fangfang1,2()   

  1. 1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    2. Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
    3. College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
  • Received:2023-07-24 Online:2023-09-30
  • corresponding author: QU  Fangfang, E-mail:
  • About author:YE Dapeng, E-mail:ydp@fafu.edu.cn
  • Supported by:
    National Natural Science Foundation of Fujian(2023J01473)

Abstract:

[Objective] JUNCAO, 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. [Methods] In 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<0.05). Moreover, the visible/near-infrared spectral reflectance in the stressed group also exhibited an increasing trend compared to the control group. (2) Samples generated by the DCAGN model conformed to the distribution patterns of the original samples. The spectral curves of the generated samples retained the shape and trends of the original data. The corresponding MDA contented of generated samples consistently falling within the range of the original samples, with the average and standard deviation only decreased by 0.6650 and 0.9743 nmol/g, respectively. (3) Prior to the inclusion of generated samples, the detection performance of the three models differed significantly, with a correlation coefficient (R2) of 0.6967 for RF model, that of 0.6729 for CNN model, and that of 0.5298 for the PLSR model. After the introduction of generated samples, as the number of samples increased, all three models exhibited an initial increase followed by a decrease in R2 on the prediction set, while the root mean square error of prediction (RMSEP) first decreased and then increased. (4) The prediction results of the three regression models indicated that augmenting the sample size by using DCGAN could effectively enhance the prediction performance of models. Particularly, utilizing DCGAN in combination with the RF model achieved the optimal MDA content detection performance, with the R2 of 0.7922 and the RMSEP of 2.1937. [Conclusions] Under low temperature stress, the MDA content and spectral reflectance of the six varieties of JUNCAO leaves significantly increased compared to the control group, which might due to the damage of leaf pigments and tissue structure, and the decrease in leaf water content. Augmenting the sample size using DCGAN effectively enhanced the reliability and detection accuracy of the models. This improvement was evident across different regression models, illustrating the robust generalization capabilities of this DCGAN deep learning network. Specifically, the combination of DCGAN and RF model achieved optimal MDA content detection performance, as expanding to a sufficient sample dataset contributed to improve the modeling accuracy and stability. This research provides valuable insights for JUNCAO plants breeding and the diagnosis of low-temperature stress based on spectral technology and machine learning methods, offering a scientific basis for achieving high, stable, and efficient utilization of JUNCAO plants.

Key words: JUNCAO, visible/NIR, deep convolutional generative adversarial network (DCAGN), low temperature stress, machine learning