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Rapidly inverting soil salinity is crucial to the soil water and salt migration for the prevention of the secondary salinization. However, the accuracy and efficiency of soil salinity inversion models are hindered to the coupling relationship between water and salt in soil, particularly for time-consuming and labor-intensive soil collection. This study aims to reduce the interference of soil moisture for obtaining better sample diversity and further improving the robustness of soil salinitu inversion models by using spectral technology. A total of 113 normal and 115 saline soil samples were collected in the Xinjiang cotton fields. These samples were further subjected to different levels of wetting treatment. and subsequently 467 soil samples with varing salt and moisture contents were obtained. Soil salt content was calibrated using the conductivity of soil leaching solution. Spectral data of samples was captured using an ASD ground object spectrometer (400-1000 nm) and a near-infrared spectrometer (960-1693 nm). The soil moisture was also corrected using the external parameter orthogonalization (EPO). Additionally, deep convolutional generative adversarial networks (DCGAN) with different transposed convolution stride strategies were designed to evaluate the sample set using Fréchet Inception Distance (FID) scores. Machine learning models were employed to invert the soil salinity, including partial least squares regression (PLSR), random forest (RF), and onedimensional convolutional neural network (1D-CNN) models using VGG (VNet), EfficientNet (ENet), and ResNet (RNet) architectures. The results demonstrated that the EPO can effectively reduce the interference of moisture on the salinity, indicating the better prediction performance of different models. RNet out performed PLSR, RF, VNet, Enet, RNet, and exhibited the best performance to predict the soil salinity in cotton fields. The lightweight residual neural network without attention mechanism was more suitable for one-dimensional hyperspectral data. There was an increase in the convolution stride and kernel length of the deep convolutional adversarial generative network. The better samples were obtained for the hyperspectral data with long sequences. The superior FID scores were achieved in the generated augmented sample set using Generator B (designed with the larger convolution stride and kernel size), compared with the rest. Specifically, the FID scores were reduced by 7.9% and 13.4%, respectively, compared with GA and GC. The weight distribution of attention was optimized after expanding the training set by DCGAN. The stability and accuracy of the model were further enhanced to predict the soil salinity under certain constraints on training samples. The EPO-DCGAN(GB)-RNet (called EPO-DCGAN-RNet) model was achieved in the superior RMSE and R² values of 136.472 μS/cm and 0.910, respectively, on the validation set, compared with the EPO-SG-RNet (using SG filtering denoising) and EPO-RNet (without sample augmentation). Furthermore, 1D-CNN with Grad CAM was employed to identify the characteristic bands of soil conductivity in the soil leaching solution of cotton field. In summary, an accurate inversion model EPO-DCGAN-RNet was constructed for inversing soil salinity in the cotton fields using spectral technology. Water correction and sample augmentation were incorporated for the soil salt composition. The improved model has the promising potential to the salt-tolerant cotton varieties and irrigation strategies using slightly salty water in cotton fields [ABSTRACT FROM AUTHOR]

[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 origi‐ nal 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 com‐ bination 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 DC‐ GAN 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. [ABSTRACT FROM AUTHOR]