2-(3,4-dichlorophenoxy)ethyl triethylamine (DCPTA) serves as one of the most important regulators to improve the stress resistance of crops. Taking the corn seeds as the research object, this study aims to investigate the effects of different DCPTA concentrations on drought stress in plant growth. Distilled water was taken as the control group. A 10% concentration of polyethylene glycol 6000 (PEG-6000) solution was as the control group of drought stress. The drought environment was then simulated during the growth of corn seeds. Three types of 10% PEG-6000 solutions were mixed with DCPTA at the concentrations of 0.5, 1.0, and 1.5 mg/L as three experimental groups. Corn seeds were also soaked in five solutions. Three experiments were conducted to verify the alleviating effect of DCPTA on maize seeds under drought stress. Firstly, the seed standard germination test was carried out to determine the germination rate, germination potential, as well as the average embryo and radicle length of maize seeds on the 8th day of germination. The drought resistance index of seed germination was also calculated for four experimental groups, except for the distilled water group. These germination indicators were selected to clarify the effects of DCPTA concentrations on the germination of corn seeds. Two LF-NMR experiments were used to reveal the internal causes of this pattern from a molecular perspective. Low-field nuclear magnetic resonance (LF-NMR) technology was used to obtain T₂ relaxation spectra during maize seed germination. According to the differences in T₂ relaxation spectra among five experimental groups, the differences were then obtained in the internal water and water migration patterns during maize seed germination in the solution environments. Finally, magnetic resonance imaging (MRI) technology was used to obtain a proton density-weighted image of corn seeds. The pseudo-color image was then obtained using pseudo-color processing. The color and area feature parameters were further extracted from the pseudo-color images. The color feature parameters R, G, and the sum of R and G were normalized by area in the pseudo color images. The trend was achieved in the changing pattern of water distribution in corn seeds. The experimental results showed that the germination rate of corn seeds increased by 34%, 20%, and 12%, respectively, while the germination potential increased by 18%, 8%, and 6%, respectively, and the germination drought resistance index increased by 26.29%, 16.33%, and 9.96%, respectively, as the addition of 0.5, 1.0, and 1.5 mg/L DCPTA increased, compared with the drought stress group. The length of the embryo and radicle also increased in the maize seeds. According to the T₂ relaxation spectrum of corn seeds, DCPTA reduced the proportion of semi-bound water in corn seeds, while increasing the proportion of free water and the content of total water, thereby promoting the transition of corn seeds to a more fluid state with a better conditions for the germination of corn seeds. According to the changes in the color characteristic parameters of corn seeds, DCPTA can be expected to make the water distribution of corn seeds more concentrated at the center of the embryo, thus promoting the germination of corn seeds. The relieving effect of DCPTA on drought stress in the maize seeds showed a trend of first increasing and then decreasing with the increase of the concentration. The most significant performance was found in the treatment with 1.0 mg/L DCPTA, followed by 1.5 and 0.5 mg/L DCPTA solutions. There was the highest proportion of free water, total water content, and the rightward shift of the T₂ relaxation spectrum. Therefore, DCPTA can be used to improve the drought resistance of corn seeds during germination, and then accelerate the storage rate of internal water, in order to promote the transformation of semi-bound water into bound water inside corn seeds. The necessary water conditions can be provided for the seed germination, thus reducing the damage caused by drought stress to corn seeds. This finding can greatly contribute to the internal factors that mitigate the effects of DCPTA on maize seeds. An important way can offer to improve the drought resistance in maize seeds.

The oil content of soybeans can directly dominate oil extraction and breeding. This study aims to rapidly detect and then identify the oil content of soybeans. Excellent seeds were provided for the soybean oil extraction and breeding. A systematic investigation was also implemented to construct the optimal detection model for the soybean oil content. Different dimensional low-field nuclear magnetic resonance (LF-NMR) spectrum was used to detect the oil and water content of soybeans, according to the national standard. The magnetic resonance imaging combined with deep learning was then used to establish a detection model for the high and low oil content. Two-dimensional LF-NMR was introduced to qualitatively analyze the component overlap that failed to be distinguished in the one-dimensional spectrum. The experimental results showed that the LF-NMR oil and water content software performed best to more rapidly and accurately detect the oil content of soybeans. The T1-T2 2D-NMR spectrum was used to successfully treat the overlapping of the signals of not easily flowing water, free water and oil. It was an excellent tool to supplement one-dimensional descriptions, where the water and oil signals of soybean kernels were visually distinguished. At the same time, a novel analytical approach was used to offer new perspectives on soybean trade and breeding with great promise. The U-net++ deep learning model was utilized to train the sagittal, coronal, cross-sectional, and three-sided mixed datasets of MRI imaging. Among them, the cross-sectional evaluation indexes were better compared with other datasets, where the mIoU in the semantic segmentation part was about 0.9058, with a global accuracy rate of 0.9980. The trained model was used to identify and segment the soybean MRI images, and then rapidly identify the oil content rate from the oil content level. The cross-sectional datasets were taken as the segmentation inputs for the magnetic resonance imaging, indicating the more reliable and accurate. The model, when combined with the data, allowed more information to be extracted from the MRI image than that commonly seen from subjective observations or simple signal accrual. Different datasets with magnetic resonance images of soybean cross sections were the most suitable for image segmentation. The experiment demonstrated that the LF-NMR and MRI system rapidly and non-destructively captured the information on soybean oil content, which provided a new idea and technical support to the high oil breeding of soybeans. This system can also integrate the previously fragmented detection with the LF-NMR and MRI technology, particularly for subjective observation. Deep learning can be combined to reduce the subjectivity with the scientific rigor at the same time. This LF-NMR application can be extended into the field of food and agriculture. The finding can also provide data support and theoretical guidance for the study of seed breeding, processing and storage. Meanwhile, the LF-NMR non-destructive testing can be expected to be widely promoted as a novel research tool.

Moisture and oil content of Xanthoceras sorbifolia Bunge can dominate its breeding, processing, and storage in fruit production. This study aims to explore a non-destructive, rapid, and accurate detection of the moisture and oil content of Xanthoceras sorbifolia Bunge, providing excellent seeds during oil extraction and breeding. The peak overlapping was found during detection using low-field nuclear magnetic resonance (LF-NMR). Two ways were then selected to validate the overlapping moisture and oil signals in food products. One was the removal of the water signal using LF-NMR with dry sampling. The water was evaporated to eliminate the overlapping area and then measure the oil content. Another was to separate the oil and moisture signals using low-field two-dimensional nuclear magnetic resonance (LF-2D-NMR). A series of experiments were conducted to verify the feasibility of two techniques. The national standard oven drying was used to detect the moisture, while Soxhlet extraction was used to detect the oil. The benchmark values were then obtained to verify the accuracy of nondestructive testing. The test results show that anomalous data was using LF-NMR with dry sampling, compared with the national standard. Matlab platform was used to extract the eigenvalues from each color of the Lab in the MRI pseudo-color map. The collected signals were shifted from outside to inside during drying. The physicochemical test revealed that there were abnormal changes inside the Xanthoceras sorbifolia Bunge. Since LF-NMR failed to differentiate and quantify the overlapping NMR signals in the samples, dry sampling was used to consider the anomalous variations behind these signals. As such, LF-2D-NMR was better performed to detect the oil and moisture content of Xanthoceras sorbifolia Bunge, particularly for the overlapping signals in the one-dimensional spectra. It was found that the drying induced the molecular rupture in the internal molecules of Xanthoceras sorbifolia Bunge, leading to the increasing values of saturated fatty acid and carbonyl. The hazardous substances were formed to explain the abnormal peaks in the LF-NMR data. Meanwhile, there was a significant linear correlation between the peak areas of the T₁-T₂ spectra of Xanthoceras sorbifolia Bunge that was measured by LF-2D-NMR. The moisture and oil contents were obtained after oven drying and Soxhlet extraction. The correlation coefficients of R² after verification were 0.920 9 and 0.942 4, respectively. Therefore, the LF-2D-NMR technique can be expected to quantitatively analyze the moisture and oil contents of Xanthoceras sorbifolia Bunge. This finding can provide the theoretical reference of the LF-NMR technique for the high detection accuracy in practical production.