Are yarn quality prediction tools useful in the breeding of high yielding and better fibre quality cotton(Gossypium hirsutum L.)?

Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection was conducted first to keep those with improved fibre quality,and followed for high yields,a large proportion in the resultant populations was the same between selections based on Cottonspec predicted yarn quality and HVI-measured fibre properties.They both exceeded the selection based on FQI and Background The approach of directly testing yarn quality to define fibre quality breeding objectives and progress the selection is attractive but difficult when considering the need for time and labour.The question remains whether yarn prediction tools from textile research can serve as an alternative.In this study,using a dataset from three seasons of field testing recombinant inbred line population,Cottonspec,a software developed by the Commonwealth Scientific and Industrial Research Organisation(CSIRO)for predicting ring spun yarn quality from fibre properties measured by High Volume Instrument(HVI),was used to select improved fibre quality and lint yield in the population.The population was derived from an advanced generation inter-crossing of four CSIRO conventional commercial varieties.The Cottonspec program was able to provide an integrated index of the fibre qualities affecting yarn properties.That was compared with selection based on HVI-measured fibre properties,and two composite fibre quality variables,namely,fibre quality index(FQI),and premium and discount(PD)points.The latter represents the net points of fibre length,strength,and micronaire based on the Premiums and Discounts Schedule used in the market while modified by the inclusion of elongation.PD points.Conclusions The population contained elite segregants with improved yield and fibre properties,and Cottonspec predicted yarn quality is useful to effectively capture these elites.There is a need to further develop yarn quality prediction tools through collaborative efforts with textile mills,to draw better connectedness between fibre and yarn quality.This connection will support the entire cotton value chain research and evolution.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Evaluation of Yarn Quality and Optimization of Cotton Spinning Parameter Based on Factors Analysis

In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness unevenness and tenacity level, are extracted from the seven yarn-quality indexes. The accumulative contribution percentage of the two factors is up to 91.813%,and much information in the yarn-quality indexes is reflected by the two factors. Then the score of each factor is calculated to evaluate the quality of yarn. Based on that, the techniques are optimized. The result is well in line with spinning practices, so it is testified feasibly to use this method to optimize spinning parameter.

Yarn Properties Prediction Based on Machine Learning Method

Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.