Quality grading of raw cotton based on image feature selection
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Nowadays, consumer demand more on good quality agricultural product and the grading system is used to determine the product quality. Manual grading system is commonly used in different agricultural product but manual grading system is time consuming, low efficient and labour intensive. To overcome the limitations of manual grading system, automatic grading system based on image processing technique and machine vision technique have been developed and gradually phase out the manual grading system. Besides, machine learning techniques have been combined together with automatic grading system to increase the accuracy in classification. Tomato is one of the most familiar agricultural products in Malaysia to its high market value in local and global market. The quality of tomato can be determined by the colour, appearance and size and the colour is the most considered factor for the consumer. The colour of tomato can be classified in to 6 classes which are green, breakers, turning, pink, light red and red. There are different type of automatic tomato grading system have been proposed by the researchers and the method or technology used are different. The objective of this research is to design and develop an automatic tomato grading system with most effective image processing technique and the most suitable machine learning algorithm by using MATLAB. The tomato images captured will undergo a series of image processing and used as inputs for the machine learning classifier. Then the classifier will classify the tomato into different group according to the surface colour. The results obtained are expected to display on MATLAB GUI.
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Research automatic apples grading.Single information source apple classification can not fully reflect the quality of apple grading accuracy rate,and the accuracy is low and unstable.In order to improve apple grading accuracy rate,the paper proposed an apple grading method based on the evidence theory fusion.Firstly,image processing technology was used to extract the size,shape and color features to describe the quality of apple,then RBF neural network was used for single feature for preliminary classification.The preliminary classification results were taken as evidences,and the evidence theory was used for the fusion of preliminary classification results to achieve apple apple automatic grading grading.Simulation results show that the feature fusion classification method can improve the correct rate of apple automatic grading.Compared with a single feature classification,the grading accuracy rate is higher,ansthe stability is better.
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In order to avoid the errors brought by human grading,the paper discussed the tea quality grading by the image processing technique,including image segmentation,labeling and HSI color model of.Results showed that the technique successfully segmented the tea images and distinguished the tea grades by using the H and S component histograms in the HSI color model.
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With large-scale production and the need for high-quality tomatoes to meet consumer and market standards criteria, have led to the need for an inline, accurate, reliable grading system during the post-harvest process. This study introduced a tomato grading machine vision system based on RGB images. The proposed system performed calyx and stalk scar detection at an average accuracy of 0.9515 for both defected and healthy tomatoes by histogram thresholding based on the mean g-r value of these regions of interest. Defected regions were detected by an RBF-SVM classifier using the LAB color-space pixel values. The model achieved an overall accuracy of 0.989 upon validation. Four grading categories recognition models were developed based on color and texture features. The RBF-SVM outperformed all the explored models with the highest accuracy of 0.9709 for healthy and defected category. However, the grading accuracy decreased as the number of grading categories increased. A combination of color and texture features achieved the highest accuracy in all the grading categories in image features evaluation. This proposed system can be used as an inline tomato sorting tool to ensure that quality standards are adhered to and maintained.
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Machine vision based grading for agricultural crops has been well developed and accepted as an attractive grading method. However, machine vision based grading for eggplant fruit is not available yet. This study reports on the attempt to develop an eggplant grading machine using six CCD cameras as the sensing device. Feature extraction algorithms were developed to extract eggplant's features, i.e., length, diameter, volume, curvature, color homogeneity, calyx color, calyx area, and surface defect. The system could acquire six images per fruits covering the entire surface of the eggplant fruits. An agreement rate of 78.0% was achieved in the feasibility study where the machine vision based grading was compared with manual grading. The throughput of the developed system was 0.3 second per fruit. Details of the system, an outline of the algorithm, and performance results are reported in this article.
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To obtain the characteristics that can be used for grading tobacco leaves is a crucial step in the process of an automatic tobacco image grading system.This article first expounds the current national grading standard of tobacco leaves,and then explores the feasible methods which is suitable for industrial environment after analyzing techniques using the characteristics of the color,shape,texture and other technical measure,with considering of simplicity,economy,efficiency,and reliability.The method solved the problems in LED light,image feature extracting and choice of well distributed color model.
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The trash and color of raw cotton are very important and decisive factors in the current cotton grading system. In this paper, an image system is developed that can characterize trash from a raw cotton image captured by a color CCD camera and acquire color parameters. The number of trash particles and their content, size, size distribution, and spatial density can be evaluated after raw cotton images of the physical standards are thresholded and connectivity is checked. The color grading of raw cotton can be influenced by trash if the image of raw cotton includes trash. Therefore, the effect of trash on color grading is investigated using a color difference equation that measures the color difference between a trash-containing image and a trash-removed image. Color grading of raw cotton involves a trained artificial neural network, which turns out to have a good classifying ability, suggesting that the application of an artificial neural network for color grading is highly valid.
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