Zhe Liu

An Automatic Counting Method of Maize Ear Grain Based on Image Processing

Corn variety testing is a process to pick and cultivate a high yield, disease resistant and outstandingly adaptive variety from thousands of corn hybrid varieties. In this process, we have to do a large number of comparative tests, observation and measurement. The workload of this measurement is very huge, for the large number of varieties under test. The grain numbers of maize ear is an important parameter to the corn variety testing. At present, the grain counting is mostly done by manpower. In this way, both the deviation and workload is unacceptable. In this paper, an automatic counting method of maize ear grain is established basing on image processing. Image segmentation is the basis and classic difficult part of image processing. This paper presents an image pre-processing method, which is based on the characteristics of maize ear image. This method includes median filter to eliminate random noise, wallis filter to sharpen the image boundary and histogram enhancement. It also mainly introduces an in-depth study of Otsu algorithms. To overcome the problems of Otsu algorithm that background information being erroneously divided when object size is small. A new method based on traditional Otsu method is proposed, which combines the multi-threshold segmentation and RBGM gradient descent. The implementation of RBGM gradient descent leads to a remarkable improvement on the efficiency of multi-threshold segmentation which is generally an extremely time-consuming task. Our experimental evaluations on 25 sets of maize ear image datasets show that the proposed method can produce more competitive results on effectiveness and speed in comparison to the manpower. The grain counting accuracy of ear volume can reach to 96.8%.

Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability

The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra. The bands at 1300–1470, 1768–1949, 2010–2064, and 2235–2311 nm were mainly affected by the producing area. Band selection and principal component analysis were applied to improve the influence of variety on NIR spectra by processing the pretreated spectra. The degrees of the influence of varieties, producing areas, ears, and ear positions were calculated, and the percentages of the influence of varieties, producing areas, ears, and ear positions were 45.40%, 42.66%, 8.22%, and 3.72%, respectively. Therefore, genetic differences among maize inbred lines are the main factors accounted for NIR spectral differences. Producing area is a secondary factor. These results could provide a reference for researchers who authenticate varieties, perform geographical origin traceabilities, and conduct maize seed breeding.

Advances in crop phenotyping and multi-environment trials

Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques, while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper, we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as green- houses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environ- ments, especially for traits that are difficult to be tested in multi-environment trials (MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmers cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.

Method and System of Maize Hybridized Combination Based on Inbred SSR and Field Test

Molecular breeding is considered an important way to improve the breeding efficiency. But due to the deletion of data, method and instrument, molecular design breeding is basically at the concept stage, without operational technology process and breeding practice. On the basis of breeding data from Beijing Kings Nower Seed ST secondly, the heterosis rate of the parents was got by calculating both the genetic differences and phenotypic differences of the parents according to the SSR detection results and field testing results respectively to express the special combining ability of their own; finally, in this paper it constructed the hybridization group model by using the comprehensive characters, special combining ability, orthogonal anti value, calculated the comprehensive characters, advantages, disadvantages of the hybridizations, and screened hybrid combinations to the next round field breeding, it also developed a software system of hybrid combination to support the technology route. Applying the software, 37 hybrid combinations resistance to Ralstonia solanacearum were got based on 179 inbred lines with molecular and phenotypic data. Thus, the method and software preliminary provides technical support for our country to carry out and perfect the molecular design breeding.

Using Memcached to Promote Unified User Management System

In modern society, more and more companies have introduced different systems in corporate business sectors, furthermore, an employee may work in various systems. As a result, the employees need to record multiple usernames and passwords, which brings discomfort and lots of likelihood of errors, and posts security risks for the different systems that have their own security user authentications. Meanwhile, most of management systems are based on B/S model, where one of the foremost requirements is to provide low-latency access to frequent and large amounts of requests. To address this needs, in this paper, we firstly demonstrate the design of a web unified user management system which based on the Role-based access control (RBAC) model and Memcached. Secondly we conducted an experiment on two groups of integrated prototype systems to validate our results. Finally we draw a conclusion than it can not only monitor users in real time and configure the permissions safely and flexibly, but also reduce the response time significantly and improve the system performance.

Assessment of wind-induced environmental lodging stress for maize based on GIS

Lodging in maize is one of the major problems in maize production worldwide. This study is to assess environmental lodging stress for maize based on probability analysis of extreme wind event in maize vegetative stage. A total of 687 growing counties in Huang-Huai-Hai Plain, China were chosen as study area. There were 148 meteorology stations with daily extreme wind speed data in recent 59 years. At first, for each station, the maximum value of daily extreme wind speed in maize vegetative stage (MEWSV for short) was calculated yearly, and the mean and standard deviation of MEWSV in all stations were interpolated into all growing counties. Then, the probability distribution of MEWSV was simulated using Gumbel distribution and Normal distribution, and the result showed that Gumbel distribution was better. At last, for each growing counties, the probability of extreme wind event (that MEWSV was equal or higher than 19m/s) was calculated based on Gumbel distribution, and the assessed stress values were divided into 5 levels and visualized in GIS using a thematic map. It showed us clearly that most growing counties in the northwest of the study area had very severe lodging stress. In order to validate the obtained results, some field survey data were used in current study and it showed that they were consistent in general. But this method using meteorology data to indirectly measure the environmental lodging stress is less costly and more operational than the traditional field-based survey approach, especially when the region to be evaluated is very large. This study can facilitate the identification of better-adapted growing environments, so as to reduce the risk and loss of lodging in maize.

Predicting the yield of varieties of maize in a target environment using regression analysis

Previous analyses on variety yield have usually focused on regression coefficients as an indicator to measure the stability and adaptation of a specific variety under experimental conditions. Due to the huge differences between experimental plots and farm fields, the model results from experimental plots can hardly be applied to farm fields. In this study, a regression analysis was conducted between the variety yield and an on-trial environment index (the mean yield of all varieties in the same test site). Then, using the average proportional coefficient between the on-trial environment index and the on-farm environment index (the statistical maize production yield of the growing county containing the test site) as a bridge, the on-farm environment index was converted to the corresponding on-trial environment index, which was then applied to the regression model generated from the on-trial plot-scale data. This procedure ensured the homogeneity of the model parameters and successfully predicted the yield of maize varieties under a target environment. The procedure also produced the 95% confidence interval predicted yield, making the results more practically significant. By introducing the proportional coefficient and confidence interval, the new approach provides a feasible solution for studying the performance of varieties under on-farm conditions. Finally, we used the maize variety NH1101 as an example to illustrate the modeling procedures. The results indicated that the model produced promising results. The new method provides direct support for variety recommendation, and facilitates the identification of better-adapted varieties.