Real time Fruit Classification using Jetson Nano -nvidia jetson nano projects

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Real time Fruit Classification using Jetson Nano -nvidia jetson nano projects

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Real time Fruit Classification using Jetson Nano -nvidia jetson nano projects

This project proposes a new approach for real-time fruit detection, combining a fast geometrical pre-processing whose output feeds a convolutional neural network (CNN) classifier. The first step is a radial Hough-like operator, which aims at identifying quickly the regions of interest, restricting the use of the CNN to the most probably genuine candidates. The proposed method is generic enough to be applied on most near-spherical fruits. It was tested in two contexts: grapes and apples, with different varieties and phenological stages. In both cases the proposed method provided promising results. Correlation coefficients with manual counting and real harvest loads are up to 0.96 for grapes and up to 0.85 for apples

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Weight 1.000000 kg

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