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- W2797133056 abstract "Agriculture is an integral part of economic development, and thus, it becomes essential to lift the impact factor of agriculture development. In past years, researchers had introduced many nondestructive image processing technique to grade the food products. These techniques ensure the quality of food products, are consistent, and save the labor time as well. Many dedicated systems or techniques are available for grading particular type of fruit; therefore, there is need to devise common technique to grade various type of fruits. This paper introduces the common feature extraction method which uses local tetra pattern to grade fruits. In this research, we graded guava fruit into four categories (unripe, ripe, overripe, and defected). The performance of the proposed method is evaluated and compared using ensemble classifiers and compared using accuracy and error rate. The experimental results showed the highest accuracy of 93.8% by Subspace Discriminant Ensemble classifier. The proposed method can be easily adapted for any other spherical fruit or vegetable." @default.
- W2797133056 created "2018-04-24" @default.
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- W2797133056 date "2018-01-01" @default.
- W2797133056 modified "2023-09-27" @default.
- W2797133056 title "Local Tetra Pattern-Based Fruit Grading Using Different Classifiers" @default.
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- W2797133056 doi "https://doi.org/10.1007/978-981-10-7566-7_28" @default.
- W2797133056 hasPublicationYear "2018" @default.
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