Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300–1400 nm and secondarily concentrated at approximately 1050–1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.

Low temperature is the primary factor to affect maize sowing in early spring. It is, therefore, vital for maize breeding programs to improve tolerance to low temperatures at seed germination stage. However, little is known about maize QTL involved in low-temperature germination ability. 243 lines of the intermated B73×Mo17 (IBM) Syn4 recombinant inbred line (RIL) population was used for QTL analysis of low-temperature germination ability. There were significant differences in germination-related traits under both conditions of low temperature (12°C/16h, 18°C/8h) and optimum temperature (28°C/24h) between the parental lines. Only three QTL were identified for controlling optimum-temperature germination rate. Six QTL controlling low-temperature germination rate were detected on chromosome 4, 5, 6, 7 and 9, and contribution rate of single QTL explained between 3.39%~11.29%. In addition, six QTL controlling low-temperature primary root length were detected in chromosome 4, 5, 6, and 9, and the contribution rate of single QTL explained between 3.96%~8.41%. Four pairs of QTL were located at the same chromosome position and together controlled germination rate and primary root length under low temperature condition. The nearest markers apart from the corresponding QTL (only 0.01 cM) were umc1303 (265.1 cM) on chromosome 4, umc1 (246.4 cM) on chromosome 5, umc62 (459.1 cM) on chromosome 6, bnl14.28a (477.4 cM) on chromosome 9, respectively. A total of 3155 candidate genes were extracted from nine separate intervals based on the Maize Genetics and Genomics Database (http://www.maizegdb.org). Five candidate genes were selected for analysis as candidates putatively affecting seed germination and seedling growth at low temperature. The results provided a basis for further fine mapping, molecular marker assisted breeding and functional study of cold-tolerance at the stage of seed germination in maize.

Deep-sowing is an effective measure to ensure seeds absorbing water from deep soil layer and emerging normally in arid and semiarid regions. However, existing varieties demonstrate poor germination ability in deep soil layer and some key quantitative trait loci (QTL) or genes related to deep-sowing germination ability remain to be identified and analyzed. In this study, a high-resolution genetic map based on 280 lines of the intermated B73×Mo17 (IBM) Syn10 doubled haploid (DH) population which comprised 6618 bin markers was used for the QTL analysis of deep-sowing germination related traits. The results showed significant differences in germination related traits under deep-sowing condition (12.5 cm) and standard-germination condition (2 cm) between two parental lines. In total, 8, 11, 13, 15, and 18 QTL for germination rate, seedling length, mesocotyl length, plumule length and coleoptile length were detected for the two sowing conditions, respectively. These QTL explained 2.51-7.8% of the phenotypic variance with LOD scores ranging from 2.52 to 7.13. Additionally, 32 overlapping QTL formed 11 QTL clusters on all chromosomes except for chromosome 8, indicating the minor effect genes have a pleiotropic role in regulating various traits. Furthermore, we identified six candidate genes related to deep-sowing germination ability, which were co-located in the cluster regions. The results provide a basis for molecular marker assisted breeding and functional study in deep-sowing germination ability of maize.

Masson pine (Pinus massoniana Lamb.) is an important timber species and is widely planted in southern China. A large quantity of seed is required for sowing eac

To improve seed germination of Chinese fir (Cunninghamia lanceolata (Lamb) Hook), IAA and GA₃ treatments were applied. The most significant promotion by IAA and GA₃ occurred at the concentration of 10⁻⁴ M and 10⁻⁵ M, respectively. 10⁻⁴ M IAA treatment increased germination rate, germination index, seedling length, seedling weight by 41%, 47%, 30%, 28% compared to the control, and 12%, 23%, 15%, 11% compared to target moist chilling. Seed vigor and endogenous hormone levels were analyzed to elucidate the physiological mechanisms. Electrical conductivity test showed that electrolyte leakage rate of the control seeds was 10.4 µS.cm⁻².g⁻¹ while it was decreased to 5.6 µS.cm⁻².g⁻¹ after 10⁻⁴ M IAA soaking. Furthermore, oxygen sensing test showed that 10⁻⁴ M IAA treatment increased oxygen metabolism rate (OMR) by 17% and it reduced increased metabolism time (IMT) by 31%, critical oxygen pressure (COP) by 62%, relative germination time (RGT) by 36%, suggesting that IAA promoted seed respiration. In addition, gas chromatography/mass spectrometry analysis showed that IAA soaking reduced endogenous IAA levels by 32% while it increased endogenous GA₁ contents by 347%, suggesting that feedback inhibition of IAA biosynthesis and promotion of GA₁ biosynthesis might be involved in seed vigor and germination improvement.