By Yueling Zhao, Haiyan Han, Liying Cao, Guifen Chen (auth.), Daoliang Li, Yingyi Chen (eds.)
The three-volume set IFIP AICT 368-370 constitutes the refereed post-conference lawsuits of the fifth IFIP TC five, SIG 5.1 overseas convention on laptop and Computing applied sciences in Agriculture, CCTA 2011, held in Beijing, China, in October 2011. The 189 revised papers awarded have been conscientiously chosen from a variety of submissions. They disguise a variety of attention-grabbing theories and functions of knowledge know-how in agriculture, together with simulation types and decision-support structures for agricultural creation, agricultural product caliber trying out, traceability and e-commerce know-how, the appliance of knowledge and verbal exchange know-how in agriculture, and common details provider expertise and repair platforms improvement in rural parts. The sixty eight papers integrated within the moment quantity concentrate on GIS, GPS, RS, and precision farming.
Read or Download Computer and Computing Technologies in Agriculture V: 5th IFIP TC 5/SIG 5.1 Conference, CCTA 2011, Beijing, China, October 29-31, 2011, Proceedings, Part II PDF
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Extra resources for Computer and Computing Technologies in Agriculture V: 5th IFIP TC 5/SIG 5.1 Conference, CCTA 2011, Beijing, China, October 29-31, 2011, Proceedings, Part II
A total of 110 spectra of NIR or Raman were acquired for each adulterant. The same sets of samples were used for enabling comparison of the spectral techniques. For each sample, four of 5 measurements were used for the development of calibration model and rest for validation. Hence a total of 264 individual samples were used for calibration and 66 for validation. 3 Results and Discussion As we know, every oil differs in composition, length and unsaturated degree of the fatty acids as well as their positions in the chain.
Therefore, the PCA and PLS-DA method are applied in the study to identify the unknown adulterations in sesame oil samples. An overall PCA was carried out using all samples of each oil. Fig 2 shows the classification results of the PCA on data sets containing three kinds of the oil spectra in score plots. The PCA model was achieved using 1PCs and explaining 97% of variance in the spectral data. The plots shows that the peanut oil is clearly separated from the others. But corn oil has so many portion in common with soybean oil, which gives no boundaries.
4. 3 Fig. 5. Relationships between predicted and ture values of calibration set in milk stage Error Analysis and Model Evaluation There are several factors in source of the error. The first reason is that the smoothness, cleanliness of leaves and the external environment changing make a great influence on the reflectance spectra. And in addition, water lost in the process of measurement and hard to achieve constant weight in drying attribute to another reasons. These measurement errors accumulate eventually to cause differences in the results of the quantitative analysis.
Computer and Computing Technologies in Agriculture V: 5th IFIP TC 5/SIG 5.1 Conference, CCTA 2011, Beijing, China, October 29-31, 2011, Proceedings, Part II by Yueling Zhao, Haiyan Han, Liying Cao, Guifen Chen (auth.), Daoliang Li, Yingyi Chen (eds.)