Rice seed identification by computerized AFLP-DNA fingerprinting

We developed a computerized seed identification system. Fifteen rice varietiesthat were widely used in China were analyzed by AFLP fingerprinting. 12 primerpairs were screened, In order to simplify the procedure and cut down the cost inseed identification. the least number of primer pairs for practical seed identifi-cation should be seleeled. In this study. 3 primer pairs were selected. They

Coleoptile Purple Line Regulated by A-P Gene System Is a Valuable Marker Trait for Seed Purity Identification in Hybrid Rice

In plants,a large number of anthocyanin biosynthetic genes encoding enzymes and regulatory genes encoding transcription factors are required for anthocyanin synthesis.Coleoptile purple lines are two purple lines on both sides of coleoptiles after seed germination.However,the molecular mechanism of coleoptile purple line is not clear in rice so far.In this study,two major dominant genes,coleoptile purple line 1(OsCPL1,also known as OsC1)and coleoptile purple line 2(OsCPL2),were isolated via map-based cloning,and both of them were required for anthocyanin biosynthesis of coleoptile purple line in rice.The knockout and complementation experiments confirmed that OsC1 was required for purple color in most organs,such as coleoptile line,sheath,auricle,stigma and apiculus,whereas OsCPL2 was just required for coleoptile purple line.OsC1 was predominantly expressed in coleoptiles,flag leaves,and green panicles,and highly expressed in young leaves,whereas OsCPL2 was predominantly expressed in coleoptiles,and extremely lowly expressed in the other tested organs.Loss-of-function of either OsC1 or OsCPL2 resulted in significant reduction of transcript levels of multiple anthocyanin biosynthesis genes in coleoptiles.Coleoptile purple line was further used as a marker trait in hybrid rice.Purity identification in hybrid rice seeds via coleoptile purple line just needed a little water,soil and a small plate and could be completed within 5 d.Molecular marker and field identification analyses indicated that coleoptile purple line was reliable for the hybrid seed purity identification.Our findings disclosed that coleoptile purple line in rice was regulated by two major dominant genes,OsC1 and OsCPL2,and can be used as a simple,rapid,accurate and economic marker trait for seed purity identification in hybrid rice.

Fuzzy Inference System for Identification of Cereals Weeds Seeds

In agriculture the identification and classification of weed seeds are technically and economically important. This work bears on the study of the morphological characteristics of the widespread weeds seeds in the north east of Algeria （the Setifian high plateau）. Fourteen characteristics were used to identify ninety one species of seeds which belong to nineteen botanical families. The morphological characteristics in which the study was based on are： form, color, size, solidity, brightness, smoothness, seed length, seed width, seed caliber, outgrowths, outgrowths form, outgrowths color, outgrowths length, outgrowths width, weight per 100 seeds. Considerable differences were noticed between the various species of weeds seeds. The study of morphological characteristics of seeds allows identifying the different seeds mixed with cultivated plant, it also allows knowing the various species of weeds in fields. So such studies help to develop different strategies to control weeds.

Identification of Quantitative Trait Loci Controlling Seed Physical and Nutrient Traits in Cotton

Cotton(Gossypium spp.) is the leading fiber crop,and an important source of the important edible oil and protein meals in the world.Complex genetics and strong environmental effects hinder

Discrimination of Acacia seeds at species and subspecies levels using an image analyzer

Seeds of Acacia species and subspecies were characterized using an image analyzer and discriminated for the purpose of identification of species, using their seeds. The species considered in the study were Acacia nilotica subsp. indica, A. nilotica subsp. cupressiformis, A. nilotica subsp. tomentosa, A. tortilis subsp. raddiana, A. tortilis subsp. spirocarpa, A. raddiana, A. senegal, A. auriculiformis, A. farnesiana, A. leucophloea, A. mearnsii, A. melanoxylon, A. planifrons and A. mangium. Eight samples each consisting of 25 seeds per species were studied using the image analyzer for physical characteristics of seeds, such as 2D surface area, length, width, perimeter, roundness, aspect ratio and fullness ratio. Discriminant analysis showed that acacias can be discriminated at species and subspecies levels, with 96% accuracy. Exceptions were A. nilotica subsp. tomentosa(75.0%), A. tortilis subsp. spirocarpa(75.0%) and A. raddiana(87.5%) which had relatively low discrimination accuracy. However, discriminant analysis within selected species showed complete recognition of these species except for A. tortilis subsp. spirocarpa, that had still a large overlap with A. leucophloea. The study also revealed that both seed size and shape characteristics were responsible for species discrimination. It can be concluded that rapid analysis of seed size and shape characteristics using image analysis techniques can be used as primary and secondary keys for identification of acacias.

Classification of weed seeds based on visual images and deep learning

Weeds are mainly spread by weed seeds being mixed with agricultural and forestry crop seeds,grain,animal hair,and other plant products,and disturb the growing environment of target plants such as crops and wild native plants.The accurate and efficient classification of weed seeds is important for the effective management and control of weeds.However,classification remains mainly dependent on destructive sampling-based manual inspection,which has a high cost and rather low flux.We considered that this problem could be solved using a nondestructive intelligent image recognition method.First,on the basis of the establishment of the image acquisition system for weed seeds,images of single weed seeds were rapidly and completely segmented,and a total of 47696 samples of 140 species of weed seeds and foreign materials remained.Then,six popular and novel deep Convolutional Neural Network(CNN)models are compared to identify the best method for intelligently identifying 140 species of weed seeds.Of these samples,33600 samples are randomly selected as the training dataset for model training,and the remaining 14096 samples are used as the testing dataset for model testing.AlexNet and GoogLeNet emerged from the quantitative evaluation as the best methods.AlexNet has strong classification accuracy and efficiency(low time consumption),and GoogLeNet has the best classification accuracy.A suitable CNN model for weed seed classification could be selected according to specific identification accuracy requirements and time costs of applications.This research is beneficial for developing a detection system for weed seeds in various applications.The resolution of taxonomic issues and problems associated with the identi-fication of these weed seeds may allow for more effective management and control.

Identification of damaged corn seeds using air-coupled ultrasound

Corn,an important staple in many countries around the world,is subject to a very inefficient germination rate due to worm-damaged seeds.However,air-coupled ultrasound is a rapid,safe and widely accepted method for the early detection of such damage.In this study,the current effectiveness and future prospects of this technique for identifying damaged seeds were explored.The presented procedure started with drawing a sample of 810 seed particles,consisting of 400 that were intact,400 manually damaged and 10 damaged by worms.Then the principal component analysis(PCA)method was used to reduce the dimensions of air-coupling ultrasonic information and extract the top ten principal components.Finally,a KNN decision tree by using SIMCA software and a Fisher recognition model by using MATLAB software were constructed.The pattern recognition was established by using KNN,which has the most accurate recognition rate.The correct recognition rate of modeling for the front and back data of the intact particles was 98%and 100%,respectively;and for the manually damaged particles,99%and 97%,respectively.The results show that the model developed by using air-coupled ultrasonic data can classify corn seed particles both with and without holes to provide a basis for the development of a seed selection system,which has a significant role in improving the clarity and the germination rate.

Rice seeds identification based on back propagation neural network model

Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural network algorithm to build a rice seeds identification platform.The information on rice seed surface is collected from four angles and processed using Geomagic Studio software.Based on the noise filtering,smoothing of the point cloud,vulnerability repair,and downsampling,the three-dimensional(3D)morphological characteristics of a rice seed surface,and the projection features of the main plane cross-section are obtained through the calculation of the features.The experiments were performed on five rice varieties,including Da Hua aromatic glutinous,Hong ShiⅠ,Tian You VIII,Xin Dao X,and Yu Jing VI.The resulting input vector consisted respectively of:(1)nine 3D morphological surface features,(2)nine projection features of the main cross-section plane of rice,and(3)all of the above features.The results showed that for an input vector consisting of nine surface 3D morphological features,the recognition rate of the five rice varieties was 95%,96%,87%,93%,and 89%,respectively;for an input vector consisting of nine projection features of the main cross-section plane of rice seeds,the recognition rate was 96%,96%,90%,92%,and 89%,respectively;and lastly,for an input vector consisting of all the features,the highest recognition rate of 96%,97%,91%,94%,and 90%,respectively,was achieved.The analysis showed that rice varieties could be identified by using 3D laser scanning.Therefore,the proposed method can improve the accuracy of rice varieties identification.

Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics

False seeds can often be seen in the maize seed market,leading to a serious decline in maize yield.Those existing variety identification methods are expensive,time consuming,and destructive to seeds.The aim of this study is to develop a cheap,fast and non-destructive method which can robustly identify large amounts of maize seed varieties based on near-infrared reflectance spectroscopy(NIRS)and chemometrics.Because it is difficult to establish models for every variety in the market,this study mainly investigated the performance of models based on a large number of samples(more than 40 major varieties in the market).The reflectance spectra of maize seeds were collected by two modes(bulk kernels mode and single kernel mode).Both collection modes can be applied to identification,but only the single kernel mode can be applied to purity sorting.The spectra were pretreated with smoothing,the first derivative and vector normalization;and then principal component analysis(PCA),linear discriminant analysis(LDA)and biomimetic pattern recognition(BPR)were applied to establish identification models.The environmental factors such as producing areas and years have a significant influence on the performance of the models.Therefore,the method to improve the robustness of the models was investigated in this study.New indexes(correct acceptance degree(CAD),correct rejection degree(CRD)and correct degree(CD))were defined to analyze the performance of the models more accurately.Finally,the models obtained a mean correct discrimination rate of over 90%,and exhibited robust properties for samples harvested from different areas and years.The results showed that NIR technology combined with chemometrics methods such as PCA,LDA,and BPR could be a suitable and alternative technique to identify the authenticity of maize seed varieties.