【目的】研究并明确种植密度和植物生长调节剂对玉米茎秆性状的影响,可为合理密植、构建适宜群体结构、实现玉米高产抗逆栽培提供理论依据和技术支撑。【方法】以JK968为试验材料,设置6.0×10 ⁴株/hm ²（D1）、7.5×10 ⁴株/hm ²（D2）和9.0×10 ⁴株/hm ²（D3）3个密度水平,以及乙烯利矮壮素复配剂（EC）和喷施清水为对照（CK）2个处理,研究种植密度对玉米茎秆性状的影响以及茎秆性状对化学调控的响应。 【结果】（1）倒伏率随种植密度增加呈升高趋势,其中在D1密度条件下,JK968的倒伏率分别比D2和D3低69.1%和83.4%;EC处理可显著降低倒伏率,在D1、D2和D3密度条件下分别比对照降低了5.0%、19.8%和41.0%。（2）株高、穗位高、穗位系数和重心高度在不同种植密度和化控处理间均存在极显著差异,具体表现为随种植密度增加呈升高趋势;EC处理后显著降低了地上部第6节以下的节间长度,增加了地上部第7节以上的节间长度,株高和穗位系数略降低,而穗位高和重心高度显著降低。（3）茎秆抗折力和茎秆外皮穿刺强度在不同处理间均存在极显著差异。大喇叭口期至成熟期呈先升高后降低趋势,在乳熟期达最大值。随种植密度增加,地上部第3、4和5节茎秆抗折力和茎秆外皮穿刺强度呈降低趋势;不同节间茎秆抗折力和茎秆外皮穿刺强度表现为地上部第3节>第4节>第5节;EC处理后显著增加了地上部第3、4和5节茎秆抗折力和茎秆外皮穿刺强度。（4）穗粒数和百粒重随种植密度增加呈降低趋势;EC处理后,穗粒数、百粒重和产量均较对照增加。在D1、D2和D3密度条件下,EC处理后产量分别较对照高438.8 kg·hm ^-2、1041.3 kg·hm ^-2和3376.5 kg·hm ^-2,增幅分别为3.6%、8.2%和27.8%。 【结论】随种植密度增加,玉米株高增加、重心高度上移、基部节间伸长、基部节间充实度和抗折力下降。EC处理显著降低了地上部第6节以下的节间长度,显著增加了地上部第7节以上的节间长度,株高略降低,重心高度和穗位高显著降低,基部节间长度缩短、基部节间充实度提高,从而提高了茎秆的抗倒伏能力。由此可见,在风灾倒伏频发地区以及种植密度过大等倒伏风险较大条件下,喷施植物生长调节剂可显著增加玉米茎秆的抗折力和茎秆外皮穿刺强度,显著降低穗位高、重心高度和倒伏率,有利于玉米高产稳产。

应用传统检测技术检测种子质量难以满足快速、无损、高效的要求。随着科技的进步，通过光谱成像技术结合化学计量学方法获取被测物的光谱信息和图像信息，具有快速、无损害、高效率等优点，目前该方法应用到种子质量检测技术的各个方面。文章介绍了光谱成像技术原理，归纳了光谱成像技术检测流程中的玉米种子状态、光谱数据预处理方法、光谱数据特征波长提取方法、建模分析方法和模型评价指标，并总结了光谱成像技术在玉米种子活力、水分、病害、品质与产地等方面检测的研究进展，最后结合光谱成像技术在玉米种子质量检测中存在的数据处理效率低、模型准确率低等问题，从研究系统化、融合新算法等方面展望了光谱成像技术的发展趋势，以期为玉米种子无损检测提供参考。

机器视觉技术已广泛应用到农业生产的诸多领域。综合国内外优秀研究成果，阐述了现阶段机器视觉在农业方面应用的主要形式，介绍了机器视觉在农作物精选与质量检测、植物生长信息监测、农田视觉导航等应用方向的研究成果，通过分析其创新性的图像处理算法、机器视觉系统的组成，提出了当前机器视觉农业应用仍存在可靠性差、成本高、智能化水平不高等问题。结合当前机器视觉在各种领域的研究和应用情况，对未来机器视觉在农业应用的发展方向进行展望，认为基于嵌入式处理模块和多技术融合的机器视觉系统将成为未来主要发展趋势，以卷积神经网络为代表的深度学习模型也将成为未来图像识别的核心技术，并将极大改善目前机器视觉在农业应用存在的诸多问题。

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

种子质量是影响作物产量关键因素之一,而传统种子质量检测方法难以满足其快速检测的要求,采用光谱检测技术可有效降低种子检验成本、提高检验效率。以玉米种子为研究对象,基于玉米种子光谱检测流程,阐述光谱检测技术在玉米种子活力、含水率与病害、品种与产地等方面的研究现状及现存问题。光谱检测技术已应用于玉米种子质量检测,预测模型的准确率90%左右,但存在着系统性不够,应用局限性、数据处理效率低等问题,从研究系统化、增强实用性、融合新算法等方面分析光谱检测技术在玉米种子质量检测中的发展趋势。光谱检测技术应用于种子质量检验具有重要的理论和实际意义。

As an effective method for the nondestructive measurement of agricultural products quality, hyperspectral imaging technology has been widely studied in the field of seed classification and identification. Feature extraction and optimal wavelength selection are the two critical issues affecting the application of hyperspectral image in the field of seed identification. This study aimed to select optimal wavelengths from hyperspectral image data using joint skewness algorithm, so that they can be deployed in multispectral imaging-based inspection system for the automatic classification of maize seed. The hyperspectral images covering the wavelength range of 438～1 000 nm were acquired for 960 maize seeds including 10 varieties. After extracting the mean spectrum and entropy from the hyperspectral images, the joint skewness algorithm was used to select optimal wavelengths, and the classification models based on support vector machine were developed using the mean spectrum, entropy, and their combination, respectively. The experimental results indicated that the classification accuracy of the models developed by combination of the mean spectrum and entropy were higher than that of the mean spectrum or entropy for either full wavelengths or optimal wavelengths. The classification model for the combination of the mean spectrum and entropy based on the 10 optimal wavelengths selected by the joint skewness algorithm obtained 96.28% accuracy for test samples, with improvements of 4.30% and 20.38% over that of the mean spectrum and entropy, respectively, which was higher than the classification accuracy of the model that developed in the full wavelength (i.e., 93.47%). Meanwhile, the classification model based on joint skewness algorithm yielded the better classification accuracy than that of uninformative viable elimination algorithm, successive projections algorithm, and competitive adaptive reweighed sampling algorithm. This study made the online application of the hyperspectral image technology available for seed identification.

The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares-discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing.

In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean error were found to be 0.99, 0.000624 and 0.009%, respectively. In classifying the species, these values were found to be 0.99, 0.000184 and 0.001%, respectively. It has shown that all the results obtained from the ANN model had been in accordance with the real data.

There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS–DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS–DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Kernel moisture content at harvest stage (KMC) is an important factor affecting maize production, especially for mechanical harvesting. We investigated the genetic basis of KMC using an association panel comprising of 144 maize inbred lines that were phenotypically evaluated at two field trial locations. Significant positive or negative correlations were identified between KMC and a series of other agronomic traits, indicating that KMC is associated with other such traits. Combining phenotypic values and the Maize SNP3K Beadchip to perform a genome-wide association study revealed eight single nucleotide polymorphisms (SNPs) associated with KMC at ≤ 0.001 using a mixed linear model (PCA+K). These significant SNPs could be converted into five quantitative trait loci (QTLs) distributed on chromosomes 1, 5, 8, and 9. Of these QTLs, three were colocalized with genomic regions previously reported. Based on the phenotypic values of the alleles corresponding to significant SNPs, the favorable alleles were mined. Eight maize inbred lines with low KMC and harboring favorable alleles were identified. These QTLs and elite maize inbred lines with low KMC will be useful in maize breeding.

Seeds are central to crop production, human nutrition, and food security. A key component of the performance of crop seeds is the complex trait of seed vigour. Crop yield and resource use efficiency depend on successful plant establishment in the field, and it is the vigour of seeds that defines their ability to germinate and establish seedlings rapidly, uniformly, and robustly across diverse environmental conditions. Improving vigour to enhance the critical and yield-defining stage of crop establishment remains a primary objective of the agricultural industry and the seed/breeding companies that support it. Our knowledge of the regulation of seed germination has developed greatly in recent times, yet understanding of the basis of variation in vigour and therefore seed performance during the establishment of crops remains limited. Here we consider seed vigour at an ecophysiological, molecular, and biomechanical level. We discuss how some seed characteristics that serve as adaptive responses to the natural environment are not suitable for agriculture. Past domestication has provided incremental improvements, but further actively directed change is required to produce seeds with the characteristics required both now and in the future. We discuss ways in which basic plant science could be applied to enhance seed performance in crop production. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400–1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.

种子标准发芽试验中,为获取种子发芽和生长情况,需借助人工定时对种子的发芽率、发芽势、芽长和根长等相关指标进行统计和测量,该测定过程费时费力,且易对发芽的幼苗造成损伤。针对这些问题,该研究基于Mask RCNN(基于区域的卷积神经网络)模型和机器视觉技术设计了一种玉米种子发芽自动检测方法。首先,在玉米种子发芽试验的7 d内,每天采集模型训练和测试所需的图像,并用Labelme工具对种子位置进行标注,再利用标注图像训练种子定位模型;其次,根据模型定位出的玉米种子掩膜区域,设定一个监测种子发芽的椭圆区域,自动识别种子发芽状态;最后,利用骨架提取和深度搜索算法实现发芽种子幼苗主骨架线的提取,通过计算种子掩膜的质心坐标位置,实现芽和根长度的分别统计。结果表明,该方法能够有效识别发芽种子,实现发芽试验中玉米种子的发芽率、发芽势、芽长、根长等指标的自动统计,可为种子发芽试验的自动化管理提供技术参考。