Quality Assessment of Components of Wheat Seed Using Different Classifications Models (original) (raw)
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A Review Paper on Seed Quality Analysis Using image processing
Globally, wheat is the leading source of vegetable protein in human food, having higher protein content than other major cereals like maize and rice. In terms of total production tonnages used for food, India is currently at second rank as the main human food crop. Determining the quality of wheat is critical. Specifying the quality of wheat manually requires an expert judgment and is time consuming. Sometimes the variety of wheat looks so similar that differentiating them becomes a very tedious task when carried out manually. To overcome this problem, Image processing can be used to classify wheat according to its quality. The seed quality identification is very important in agriculture. Before boring the seed in farm it must be viewed properly and then sowed. In the current scenario the farmers are taking more efforts in their farm and also spending more time and money. But in spite of their hard work they do not get proper profit. So the technology can come for rescue here. There are certain limitations to human eye to observe the seed. So the electronic world helps us to separate the faulty seeds from quality seeds. The image processing algorithm is implemented using Matlab. The proposed technique is defined with the assistance of computerized image processing mechanism on MATLAB.
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The scope of the research comprises an analysis and evaluation of samples of rape, barley and wheat seeds. The experiments were carried out using the author’s original research object. The air flow velocities to transport seeds, were set at 15, 20 and 25 m s−1. A database consisting of images was created, which allowed to determine 3 classes of kernels on the basis of 6 research variants, including their transportation way via pipe and the speed of sowing. The process of creating neural models was based on multilayer perceptron networks (MLPN) in Statistica (machine learning). It should be added that the use of MLPN also allowed identification of rape seeds, wheat seeds and barley seeds transported via pipe II at 20 m s−1, for which the lowest RMS was 0.05 and the coefficient of classification accuracy was 0.94.