Automatic color separating system for printed fabric using the self-organizing map network approach

We can only use color numbers, color values and design to describe the color pattern of printed fabrics, which is different from woven fabrics with yarn disposition and texture as pattern determinants. Since most printed fabrics contain many different patterns nowadays, we need more than words and simple methods to describe the color patterns. The complication in pattern identification has made the analysis and comparison difficult and will have to be conducted manually. The automatic computer color separating system for printed fabrics proposed in this paper uses unsupervised learning network to automatically separate printed colors. The system first uses color scanner to pick the image of the printed fabrics and stores it as digital image. Then, it uses wavelet transformation to minify the fabric image to reduce the calculation load of color separation and also reserve the printing structure and color distribution of the original image. It also uses LAB color model to acquire characteristic value of the colors and the Self-Organizing Map Network (SOMN) to conduct color separation. According to our experimental results, this system can rapidly and automatically complete color separation and identify repeating patterns for printed fabrics’ images.     

Discharge estimation of the Shin-Yuan Canal using indirect method

This study presents an indirect method for estimating the discharge of the Shin-Yuan Canal. It includes a new tool of velocity measurement, a method of discharge computation, and some technology. When the Shin-Yuan Canal was built, the issue of discharge measurement was not considered. The Shin-Yuan Canal was co-constructed with the Wan-Dan Canal. Therefore, direct discharge measurement was impossible. This study indirectly estimated the discharge of the Shin-Yuan Canal by controlling the pump discharge through a pumping station. And a stage-discharge rating curve was created for continuous and real-time monitoring of canal discharge. In order to improve measurement accuracy and reliability, this study adopted the acoustic digital current meter (ADC) for discharge measurement. The probabilistic velocity distribution equation was used to accurately calculate the average vertical velocity and the area-velocity principle was used to estimate the water discharge of the Wan-Dan Canal. According to the measurement results, the discharge rating curve of the Shin-Yuan Canal could be accurately established with the precise device, ADC, and the technologies which could effectively measure velocity. This method should be promoted and applied to other similar discharge measurement and monitoring issues of irrigation canals.     

A multidecadal change analysis for irrigation ponds in Taoyuan,Taiwan, using multisource data

Paddy and Water Environment - A multidecadal change analysis of the irrigation ponds in Taoyuan, Taiwan, was conducted by using multisource data including digitized ancient maps, declassified...     

Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan

In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (?? value), and a radius of influence (search radius). A total of 46 rainfall stations and rainfall data between 1981 and 2010 were used in this study, of which the 12 rainfall stations belonging to the Taichung Irrigation Association (TIA) were used for cross-validation. To obtain optimal interpolation data of rainfall, the value of the radius of influence, and the control parameter-?? were determined by root mean squared error. The results show that the optimal parameters for IDW in interpolating rainfall data have a radius of influence up to 10?C30?km in most cases. However, the optimal ?? values varied between zero and five. Rainfall data of interpolation using IDW can obtain more accurate results during the dry season than in the flood season. High correlation coefficient values of over 0.95 confirmed IDW as a suitable method of spatial interpolation to predict the probable rainfall data in the middle of Taiwan.     

Improving the accuracy of models to map alpine grassland above-ground biomass using Google earth engine

Accurate modelling and mapping of alpine grassland aboveground biomass (AGB) are crucial for pastoral agriculture planning and management on the Qinghai Tibet Plateau (QTP). This study assessed the effectiveness of four popular models (traditional multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN)) with various input combinations (geospatial variables [GV], vegetation types [VT], field measurements [FM], meteorological variables [MV] and observation time [OT]) for AGB estimation based on a new framework for AGB modelling and mapping using Google Earth Engine. The results showed that the input feature of GV had a poor performance in AGB estimation (0.121 < R² < 0.591). FM improved the accuracy the most when incorporated with GV (0.815 < R² < 0.833). Although MV, VT and OT improved the accuracy (R²) only by 0.112–0.216 with an importance rank order of MV > VT > OT for machine learning models, their outputs could be used to map AGB. Grass AGB was less accurately predicted than shrub AGB, but the pooling of both VTs improved estimation accuracy (R²) by 0.171–0.269. The performance of the models followed the ranked order of DNN > ANN > SVM > MLR. DNN had the highest accuracy (R² = 0.818) using all non-field measured variables (excluding FM) as the inputs, and it was successfully applied to a new dataset (not associated with the data used in the training and testing) with a R² of 0.676. This study presents an effective and operational framework for modelling and mapping grassland AGB. Accordingly, it provides the scientific foundations to determine of sustainable grazing carrying capacity in alpine grasslands.     

Evaluation of soil erosion risk for watershed management in Shenmu watershed,central Taiwan using USLE model parameters

This study compiles the latest regional topographic data from field investigation and remote-sensing images to recalculate parameters of the universal soil loss equation (USLE) model of the Shenmu watershed; also to compensate for reduced accuracy of this model on small-scale slopes, this study incorporates soil erosion pin data which were collected periodically to measure the extent of soil erosion. Firstly, this study utilized the USLE model and soil erosion pin data to compare the soil erosion potential of the Chushui and Aiyuzi subwatersheds and concluded that soil erosion drastically increased if accumulated rainfall exceeded 200 mm; also, erosion depths were greater in the Aiyuzi subwatershed while estimated total erosion volume was higher in the Chushui subwatershed; this was attributed to the larger area of Chushui subwatershed and based on field measurements which supported the results of the USLE model. Secondly, this study utilized modified USLE model to compare the extreme event erosion resulting from typhoon Morakot which revealed that high rainfall intensity and long-duration rainfall events can generate large volume non-point sources of sediment that is estimated to far exceed 7–10 times of the annual soil erosion. Thirdly, this study related the C parameter of the USLE model to the existing land use in the Shenmu watershed using current, real data. Finally, this study established a post-typhoon Morakot soil erosion risk map composed of five categories of risk which was compared with post-event land cover to suggest high-erosion risk zones that may require further monitoring, remediation, and engineering measures to limit soil loss.     

Fabrication of homogeneous multi-walled carbon nanotube/poly (vinyl alcohol) composite films using for microwave absorption application

Poly (vinyl alcohol) (PVA)/multi walled carbon nanotubes (MWNT) nanocomposite films were fabricated and their microwave absorption behavior were evaluated using vector network analyzer in the frequency range of 8–12 GHz (Xband). The uniform, stable dispersion and well oriented MWNT within the PVA matrix were achieved through using sodium dodecyl sulfate (SDS) as dispersing agent. The surface morphology of the PVA/SDS/MWNT films was examined by scanning electron microscope (SEM). The SEM analysis of the film samples revealed the uniform appearance in the whole surfaces of the fabricated composite films. However, some roughness on the surface was observed due to the presence of MWNT in the film structure. The PVA/SDS/MWNT films show significant increase in microwave absorption which is improved by increasing the MWNT content. The PVA/SDS/MWNT nanocomposite film sample with MWNT loading of 10 wt% showed the maximum and the relatively high microwave absorption of 28.00 dB at the frequency of 8.6 GHz.     

Determination of regression model parameter for constructed wetland using operating data

Seven years of performance data from a free surface flow constructed wetland system receiving agricultural runoff were used to determine treatment performance and to develop regression and wetland design models. Removal rates by the wetland were 21–43.6% for 5-day biochemical oxygen demand (BOD₅), 49.0–58.1% for total phosphorous (TP), 24.1–46.0% for total nitrogen (TN), and 57.6–77.8% for total suspended solids (TSS). First-order area-based rate constant (k ₂₀) values for BOD₅ were 15.48 m/year in the early stage of observation and decreased to 12.00 m/year for the stable period. Similar results were found for TP, for which k ₂₀ values were 18.72 m/year in the early stage and 14.92 m/year for the stable period. For TN, k ₂₀ values in the early stage (21.32 m/year) were slightly lower than those for the stable period (38.02 m/year). Finally, TSS had values of 132.4 and 172.6 m/year in the early and stable periods, respectively. The low k ₂₀ for BOD₅ was not important for nonpoint source pollution control in the constructed wetland because these kinds of wetlands mainly focus on nitrogen and phosphorus retention. The wetland area and outlet concentration could be approximately predicted using the first-order kinetic model, but the maturity and hydraulic loading rate should be considered for more accurate prediction.     

Determination of efficient test sites for evaluation of peanut breeding lines using the CSM-CROPGRO-peanut model

Efficient testing of crop breeding lines requires a set of complementary test sites that adequately sample the target environments with minimal duplication. Such test sites have been derived from actual multi-environmental trial (MET) data, which often have a limitation with respect to their environmental coverage. However, this limitation can be overcome using a crop simulation model. The goal of this study was to determine the efficient test sites for METs of peanut breeding lines in Thailand using the CSM-CROPGRO-Peanut model. The model was used to simulate pod yield for 17 peanut lines at all peanut production areas in Thailand that included 76 locations in the early-rainy season, 39 locations in the mid-rainy season and 47 locations in the dry season for 30 years. The simulated data were used to sub-divide the locations for each season into groups using cluster analysis and the genotype plus genotype × environment (GGE) biplot method. Six sets of test sites were obtained based on different scenarios for site selection that included combinations of geographical distribution and representation of location-groups as determined by the two methods. Set 1 was based on geographic distribution. Sets 2–4 were based on location grouping by cluster analysis, but with the sites distributed in all regions (Set 2), or only in the north (Set 3) or northeast (Set 4). Set 5 consisted of the sites currently used, and Set 6 was based on location grouping by the GGE biplot. Although Sets 2 and 6 appeared to capture more genotype × location interaction than the others, performance rankings of the test genotypes were almost the same for all sets. They were, therefore, considered equally effective for breeding line evaluation. The final selection was then based on the convenience, and consequently the cost, for conducting the METs. Set 4 was considered most preferable in this regard. This study demonstrated the usefulness of a crop simulation model as a tool in determining the most efficient test sites for the evaluation of peanut breeding lines.     

Assessment of the occurrence of climate change and its effects on planting date and growth duration of rainfed wheat in the western and northwestern regions of Iran

In this study, the effect of climate change on planting date and growth duration of rainfed wheat in the west and northwest parts of Iran has been investigated. The occurrence of climate change in the region was first evaluated for the base period (1992–2018) using two nonparametric tests of Mann–Kendall and Sen's slope estimator. Then, the climatic parameters of maximum temperature, minimum temperature and precipitation were simulated under RCP4.5 scenario for the period 2019–2039 based on downscaled output data of the Community Climate System Model (CCSM4) using LARS WG software. The growth period was obtained using a linear multiple regression model, which was selected based on R-square and accounted for 87% of its total variation. The results predicted that the average annual temperature will increase by 2 °C, while the average annual precipitation will increase by 30% by the end of 2039. Planting dates were determined based on two indices combining temperature and precipitation for the base and future periods. The results showed that climate change effects at the 2039 horizon will shorten by 18 days the wheat growth period and the appropriate planting time for rainfed wheat will be reduced by 2–19 days.

     

Predicting soil moisture condition in arbitrary agricultural lands using the digital soil map and soil physical properties database

To predict soil moisture condition in arbitral agricultural lands by taking spatial distribution of soil type in a watershed into account, we investigated the possible proper methods of determining the soil hydrological parameters using available soil physical properties database of Japanese soils, “SolphyJ”, and the digital soil map. In this study, simulation of soil moisture was conducted at three neighboring locations, including monitoring sites each in Toyama and Fukui cities. The simulated results of soil moisture appeared to be improved when hydraulic parameters were obtained by fitting water retention data of SolphyJ compared to the parameters estimated by neural network with soil texture and dry bulk density data. It is probably because peculiar soil structure in each field could affect the hydraulic properties more than the soil texture. Simulation results also indicated that soil moistures are much different even if they are located in neighbors (<5 km). We concluded that combination of these two databases is useful for estimating soil hydraulic parameters and to predict soil moisture condition in arbitrary agricultural lands.     

Estimating the inverse demand function for transferring agricultural water in Taiwan

Changing climate has increasingly exacerbated droughts and floods in Taiwan; therefore, it is important to understand the actual demand of transferring Taiwan’s agricultural water. This estimation model could help the water resource agencies to develop appropriate mechanisms for transferring agricultural water, as well as bargaining tools for water-related negotiations. In this study, an inverse demand function estimation model for transferring agricultural water was established, and the water usage statistics derived from the water charge agreements, covering the period from January 1989 to December 2006 and including drought and non-drought periods, regarding the charging of water management fees and water usage fees, was applied to the estimation model in our empirical research. The agreements were made between irrigation associations and water companies, industrial water users, and science and industrial parks, for the purposes of strengthening irrigation management, building usage, and disposal of remaining water. The empirical research was conducted to estimate the demand for transferring agricultural water using double-log regression model for panel data, and analyzed with random effects models for regular conditions and drought periods. The results showed that the inverse demand function developed in this study was able to pass Largrange multiplier test, and adjusted R ² for the regression were high, fitting the random effects model showing good compatibility with the sample selection. From the results, we can verify the estimation models to forecasting models. The significant results not only prove that the model could provide important market information for the commercialization of water resources, but water resource agencies could also make use of this important information to develop suitable mechanisms for transferring agricultural water, as well as bargaining tools for negotiation of water transactions.     

The effects of feeding selectivity on the estimation of diet composition using the n-alkane technique

Abstract A limitation to using n‐alkanes in plants as faecal markers for estimating diet composition in herbivores is the small number of dietary components that it is possible to determine. One approach to overcoming this is to group species on the basis of their n‐alkane concentrations and to consider these groups as dietary components. Feeding selectivity within a group, however, may change the proportion of a particular species in the group ingested and hence its n‐alkane contribution to the diet ingested. This could potentially affect estimates of the composition of the diet. Simulations (380) of feeding selectivity within a group of herbage species were undertaken to assess the effects of selectivity on estimates of diet composition. The dataset was from a study whose aim was to estimate the proportions of the herbage component, composed of nineteen species, and of four individual browse species in the diet of red deer grazing a montado ecosystem in S. Portugal in the summer. Simulations were undertaken of total avoidance of each species in the group of herbage species, and selection of each species at 0·5, 1·5, 2·0 and 3·0 of its actual proportion within the group. Feeding selectivity had no effect on 0·36 of the outcomes of the simulations. Over 0·90 of the simulations were within ± 0·05 of the values calculated assuming no selection within the group of herbage species. It is concluded that, in the particular case studied, the effect of selection within a group on the estimates of other dietary components was relatively small and that the group of herbage species could be analysed as a single dietary component.     

Assessment of regional variation in climate on the management of dairy cow systems in Ireland using a simulation model

A dairy system simulator, Dairy_sim, was designed to assess the interactions between climate and management in spring‐calving milk production systems based on the grazing of grass pastures. The simulator comprises three main components: a grass herbage growth model, an intake and grazing behaviour model, and a nutrient demand model. The simulator was initially parameterized using the Irish National Dairy Blueprint. Sensitivity analysis indicated that the simulator was most sensitive to stocking rate, milk output per cow and nitrogen fertilizer inputs, but less sensitive to other variables. Field data from four grazing systems were used to test the simulator and it was concluded that Dairy_sim was suitable for evaluating the interaction of climate and management for rotational grazing dairy systems based on perennial ryegrass pastures with Friesian cows. The simulator, Dairy_sim, was then used to evaluate the effects of the regional climates of Ireland on system management. The results indicated that, between regions, herbage production at the same input of nitrogen may vary proportionally by 0·10 and that the length of the grazing season may vary by 0·25. It was concluded that the simulator could be a useful tool for developing region‐specific dairy production blueprints.     

用基于PCR的分子标记分析甘蓝型油菜抗感菌核病品系间的遗传距离

用离体叶接种法鉴定了甘蓝型油菜选系对菌核病的抗感性,结果表明绝大多数抗性品系与感病品种间的差异达到显著或极显著水平。以两种基于ＰＣＲ的标记估计所选１０ 份材料间的遗传距离。１９ 个随机引物扩增出了２６ 条重复性很好的ＲＡＰＤ 标记,１ 对依据植物Ｒ基因的ＮＢＳ区段设计的简并引物扩增出了３ 条多态性的带。依据２９ 个标记进行聚类分析,结果表明抗、感性材料在遗传距离为０．１２０６ 处被明显地区分开。５份抗性材料中,宁ＲＳ—１ 与中油８２１、中Ｒ８８８ 等其它抗性品系的遗传距离较远     

广西第八周期春大豆区试报告

广西第八周期春大豆区试结果表明：桂816、桂807、柳99—22和桂980725等四个品种的综合性状优于对照种桂春1号，分别增产10．2％、1．7％、1．5％和1．4％。可供广西各地引种试种示范和推产。     

A method for the rapid estimation of glycoalkaloids in potato tubers

A rapid method for estimating glycoalkaloid levels in potato tubers has been developed. It is basically a screening technique whereby large numbers of samples can be assigned to categories of alkaloid level such as very low, low, medium and high. The elapsed time is typically four hours and the per sample time 12 to 15 minutes when large numbers are run. Weighed tuber samples are cut up and blended with a volume (ml) of extracting solution equal to the weight of the sample in g. After blending for four minutes, a portion of the extract is poured on to a fluted filter. An aliquot of the filtrate is treated with an antimony trichloride reagent and the color is read at 550 nm after 15 min.     

台湾落花生生产技术之演进

落花生为台湾主要杂粮作物之一,主要栽培在云林、彰化、嘉义地区,一年可种植二期作(春作、秋作),早期缺乏良好栽培技术及优良品种,导致产量低落;现今则是经由品种改良栽培技术成熟发展,造就仍维持每年栽培面积在20～25khm2,年总产量51～70kt。台湾落花生栽培史上演进重要改变为:1、过去新品种多来自引种或选种,目前则采用杂交育种;2、荚果采收,由人工收获提升为机械采收,甚至荚果烘箱干燥取代人工日晒;3、田间由原本平畦栽培,配合机械整地、播种、灌溉及采收而改为作畦栽培,提高效能;4、病虫害防治,早期并无任何防治工作,现今则普遍被使用在整个花生生育期,以提高产量。