Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

In this study, artificial neural network (ANN) and linear regression (LR) approaches are proposed for predicting colour properties of laser-treated denim fabrics. Denim fabrics were treated under different combinations of laser processing parameters, including pixel time (μs), resolution (dot per inch) and grayscale (lightness percentage) as inputs. Colour properties, including colour yield (K/S sum value), CIE L*, a* and b* values and yellowness index were predicted as outputs in these approaches. Later, the prediction performances of two approaches were compared and the statistical findings revealed that ANN approach was able to provide more accurate prediction than LR approach, especially for L value. Moreover, among the three input variables, grayscale (lightness percentage) was found to be the most important factor affecting colour properties of laser-treated denim fabrics.     

Self-organizing map network for automatically recognizing color texture fabric nature

The method of recognizing color texture brought forth in the present study is to employ unsupervised learning network to automatically recognize the fabric type and the main texture types. Firstly, the color scanner is adopted to extract fabric image which is afterwards saved as the digital image. Secondly,CIE-Lab color model is taken to obtain the feature value and wavelet transform is utilized to display the texture of the fabric image. Thirdly, co-occurrence matrix is employed to figure out the feature values of the texture structure such as angular second moment, entropy, homogeneity, contrast. Finally, self-organizing map (SOM) network is used as the classifier. The experiment result shows that the study can automatically and accurately classify the fabric types (including shuttle-woven fabric, jersey fabric and non-woven fabric) and main texture type of the fabric (such as plain weave, twill weave, satin weave, single jersey, double jersey and non-woven fabric).     

神经网络方法在综合评价籼稻品质中的应用

基于人工神经网络理论,利用RBF神经网络模型,对2004年国家南方稻区晚籼优质稻区试品种的品质进行了综合评价.实际仿真结果表明,RBF神经网络用于米质评价是科学有效的,而且方法简便,迅速.     

Physicohemical basis for the preferential uses of certain rice varieties

Rice varieties such as IR-20, Ponni, Bhavani and IR-50 that are preferred for cooking showed larger surface area, higher water uptake at 96°C, greater cooked volume, soft gel consistency and greater elongation ratio. Rice varieties which are exclusively used for makingidli anddosai exhibited lower protein content, medium alkali score and higher percentage of total as well as soluble starch and amylopectin. Rice varieties which are used for making flakes showed hard gel consistency, higher alkali digestibility values, lower soluble amylose content and relatively higher amount of hot water-soluble reducing sugars. Varieties used for making puffed rice do not show any specific characteristics to differentiate them from the above types. All the rice varieties studied belong to the high amylose group except ASD-1 which belongs to the medium amylose group.     

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

In saline fields, irrigation management often requires understanding crop responses to soil moisture and salt content. Developing models for evaluating the effects of soil moisture and salinity on crop yield is important to the application of irrigation practices in saline soil. Artificial neural network (ANN) and multi-linear regression (MLR) models respectively with 10 (ANN-10, MLR-10) and 6 (ANN-6, MLR-6) input variables, including soil moisture and salinity at crop different growth stages, were developed to simulate the response of sunflower yield to soil moisture and salinity. A connection weight method is used to understand crop sensitivity to soil moisture and salt stress of different growth stages. Compared with MLRs, both ANN models have higher precision with RMSEs of 1.1 and 1.6 t ha⁻¹, REs of 12.0% and 17.3%, and R² of 0.84 and 0.80, for ANN-10 and ANN-6, respectively. The sunflower sensitivity to soil salinity varied with the different soil salinity ranges. For low and medium saline soils, sunflower yield was more sensitive at crop squaring stage, but for high saline soil at seedling stage. High soil moisture content could compensate the yield decrease resulting from salt stress regardless of salt levels at the crop sowing stage. The response of sunflower yield to soil moisture at different stages in saline soils can be understood through the simulated results of ANN-6. Overall, the ANN models are useful for investigating and understanding the relationship between crop yield and soil moisture and salinity at different crop growth stages.     

The use of k-means and artificial neural network to classify cotton lint

The use of High Volume Instrument (HVI) to measure cotton lint characteristics produces high dimensional data. A model which utilized Kohonen Self Organizing Maps (SOM) to visualize cotton lint HVI data, k-means clustering technique to cluster the data and Probabilistic Neural Network (PNN) for data classification was designed and tested using Kenyan cotton lint. According to the model the Kenyan cotton lint can be grouped into four clusters, which were successfully classified by using PNN with a correlation coefficient (R-value) of 1.     

Computerized color separation system for printed fabrics by using backward-propagation neural network

Textile production must be coupled with hi-tech assistant system to save cost of labor, material, time. Therefore color quality control is one very important step in any textiles, however excellent the fabric material itself is, if it lacks good color, then it may still result in dull sale. Therefore, this paper proposes a printed fabrics computerized color separation system based on backward-propagation neural network, whose primary function is to separate rich color of printed fabrics pattern so as to reduce time-consuming manual color separation color matching of current players. What it adopted was RGB color space, expressed in red, green, and blue. Analyze color features of printed fabrics, use gene algorithm to find sub-image with same color distribution as original image of printed fabrics yet smaller area, for later color separation algorithm use. In terms of color separation algorithm, this paper relied on supervised backward-propagation neural network to conduct color separation of printed fabrics RGB sub-image, and utilized PANTONE® standard color ticket to do color matching, so as to realize accurate color separation.     

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics.     

Assessing yield losses in deepwater rice due to yellow stem borer,Scirpophaga incertulas (Walker), in Bangladesh and Thailand

Yield loss from damage by yellow stem borer, Scirpophaga incertulas, was assessed in deep-water rice in Bangladesh and Thailand using five different methods. Because of the long stems and special growing conditions of the crop only three methods proved reliable: pot experiments in metal containers, potted plants exposed in the field, and floating exclusion cages in the field. Yields were reduced by 27–34%, and 1% yield loss was associated with 2% damaged stems at harvest. Yield loss was mainly due to a loss of bearing stems and lighter panicles borne by compensatory nodal tillers. A tentative damage threshold of 10% damaged stems at booting/flowering stage and 20% damaged stems at plant maturity is proposed.     

网络化水稻生产专家系统知识库的构建

农业专家系统是基于丰富的农业专家知识,并能模仿农业专家进行推理决策的智能计算机程序系统.知识库是农业专家系统的核心组成部分,是决定专家系统性能的关键.介绍了采用"Web浏览器/Web服务器/数据库系统"分布式计算体系结构的水稻生产专家系统的结构、功能和特点,详细论述了运用加权模糊产生式规则表示本领域专家们的知识和经验,构建水稻生产系统知识库的理论与方法.     

An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

This paper presents an artificial neural network (ANN) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton fabrics dyed using commercial reactive dyes. Achieving the expected efficiency in the application of stripping process is a very important aspect for the success of the reproduction. In the study, the predictions of L* and ΔE colorimetric values of stripped cotton samples for different stripping applications by artificial neural network are reported. We set up different network structures with different number of nodes in the hidden layer, the number of inputs and MSE of results as stopping criteria in order to get the best fitting model. According to the result of the best neural network models predicting L* and ΔE, we achieved 97 % of R for both of them. We are able to predict the L* value of the stripped samples using some working parameters as inputs with only 1.2 % error. We think that our results are very promising and the predictions of L* and ΔE values of stripped samples before applying any process are possible using the ANN model set up in the study, especially for L*.     

A scanner based neural network technique for color matching of dyed cotton with reactive dye

Conventional theory for color matching is Kubelka-Munk, but it fails in some situations. New intelligent procedures such as neural networks could learn the behavior of a complex system and produce accurate prediction. This paper investigates the ability of MLP (multiple layer perceptron) neural network for color matching of cotton fabric. Three reactive dyes, namely Levafix Red CA, Levafix Yellow CA and Levafix Blue CA were used for experiments. The dyed samples were scanned and L * a * b * histogram were extracted. Different neural networks were trained and tested using L * a * b * histogram of fabric’s images and also L * a * b * values (D65, 10°) of fabrics. The results were encouraging. For neural networks including the L * a * b * histogram in input vector, colorants and their concentration were predicted with a mean square error (MSE) less than 10^?5 and an average value of color difference (CMC (1:2)) less than 1.5 for approximately 80 % of testing data.     

A neural network model for prediction of strength loss in threads during high speed industrial sewing

In this paper artificial neural network (ANN) model has been designed to predict the strength loss in threads during high speed industrial sewing. Four different types of threads (Mercerized cotton, polyester staple spun, polyester-cotton core spun and polyester-polyester core spun) were taken for the study. The other input parameters include thread linear density, fabric area density, number of fabric layers, stitch density and needle size. In order to reduce the dependency of the results on a specific partition of the data into training and testing sets, a four-way cross validation tests were performed, i.e. total data was divided into training and testing set in four different ways. The predicted tenacity loss was correlated to the experimental tenacity loss and correlation coefficient between the actual and predicted tenacity loss obtained. It was observed that the neural network system is able to predict the tenacity loss of threads after sewing with good correlation and less average error. The relative contribution of each parameter to the overall prediction of the tenacity loss was studied by carrying out the sensitivity analysis of the test data set. The results of sensitivity analysis show that thread type is the most important input parameter followed by thread linear density, number of fabric layers, fabric area density, needle size and the stitch density.     

闽西北稻区双季稻改再生稻避蚊减害技术研究

通过对再生稻受稻瘿蚊危害轻的现象的研究，明确了在稻瘿蚊重发区双季稻改再生稻可以有效地控制稻瘿蚊成灾；再生稻避蚊减害的原因是因为再生芽易受害期短和可能与再生稻的株型结构特点有关；不同播期的再生稻避蚊减害效果不一样。3月20日前播种的避蚊减害效果好；及时烤田是再生稻避蚊减害的关键技术之一。     

Improvement of the agricultural effective rainfall for irrigating rice using the optimal clustering model of rainfall station network

In this study, the clustering method was applied to improve the usage of effective rainfall (ER) for irrigating rice paddy in the region managed by the TaoYuan Irrigation Association (TIA) of Taiwan. A total of 16 rainfall stations and rainfall data from 1981 to 2000 were used. A traditional area-weighted method (Thiessen polygons method) and an optimal clustering model of ER were evaluated and compared. The optimal clustering model of ER comprised self-organizing map (SOM), k-means (KM), and fuzzy c-means (FCM) clustering algorithms. To obtain optimal clustering data of ER, the clustering groups from two to five of SOM, KM, and FCM algorithms were determined using root-mean-squared-error. The results show that three algorithms with group numbers from two to five are adopted for the monthly optimal clustering model in different months. However, for the annual optimal model, 12 sub-models are assessed and then compared. The results show that the SOM clustering with groups three was the optimal model for annual ER. The optimal clustering model of ER provides a new procedure step in preparation of the irrigation scheduling for the TIA, and the amount irrigation water waste can be reduced from 770.1 to 22.3 mm/year. The planned ER using the optimal clustering model significantly improves the irrigation water use efficient in agricultural water management.     

有效提高寒地稻农科学种田意识的几点建议

２００２年６～８月寒地稻区长期的阴雨寡照、低温高湿给农作物的生长造成了极为不利的影响。在这样一个不利于农业生产的年份里 ,科学种田显示出了强大的优势。擅于科学种田的农民能把产量损失降至最小限度 ,使收益趋于平稳 ,而科技意识相对淡薄一些的农民 ,则损失惨重。辛苦一年 ,谁都希望能有一个好的收成 ,在此向广大的农民朋友指出几个误区、提出几点建议 ,希望能在今后的生产实践中对你们有所帮助。一、现今农民种田的几大误区１.选择品种不当盲目相信广告 ,过分看重宣传的产量效果 ,欲追求过高的产量 ,引种超积温晚熟品种 ,结果成熟…     

Comparison of empirical models for predicting yield loss of irrigated rice (Oryza sativa) mixed with Echinochloa spp.

Crop yield losses due to weeds can be described by empirical models. The objective of this study was to compare empirical models to predict interference by a mixed population of Echinochloa colona and E. crusgalli. in irrigated rice. Three experiments, one under field conditions and two under greenhouse conditions, were set up during the growing season of 2005/2006. The treatments tested in the field were 6 rice cultivars: BRS Atalanta, IRGA 421, IRGA 416, IRGA 417, Avaxi; and BRS Fronteira and 10 Echinochloa spp. densities, naturally present in the soil seedbank under field conditions. The variables soil cover, shoot dry weight of the Echinochloa spp. and their relative mass were evaluated in relation to the rice cultivars. The greenhouse experiments were carried out both in monoculture and replacement series to evaluate shoot dry mass and leaf area production, both for the crop and the weed species. The data obtained for the variables were analysed using linear and non-linear regression models. The fitting of the data to the empirical models varied as a function of the rice cultivars and variables tested. Among the models describing crop yield loss as a function of the evaluated variables, the single-parameter empirical model presented a better prediction than the two-parameter model. For the two-parameter models, yield loss estimation was obtained by the evaluated variable. In general, shoot dry mass of the weed was a better predictor of irrigated rice yield loss than soil cover.     

The potential of rice to offer solutions for malnutrition and chronic diseases

Elevated Na⁺ levels in agricultural lands are increasingly becoming a serious threat to the world agriculture. Plants suffer osmotic and ionic stress under high salinity due to the salts accumulated at the outside of roots and those accumulated at the inside of the plant cells, respectively. Mechanisms of salinity tolerance in plants have been extensively studied and in the recent years these studies focus on the function of key enzymes and plant morphological traits. Here, we provide an updated overview of salt tolerant mechanisms in glycophytes with a particular interest in rice (Oryza sativa) plants. Protective mechanisms that prevent water loss due to the increased osmotic pressure, the development of Na⁺ toxicity on essential cellular metabolisms, and the movement of ions via the apoplastic pathway (i.e. apoplastic barriers) are described here in detail.