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A Computer Vision System for Bangladeshi Local Mango Breed Detection using Convolutional Neural Network (CNN) Models

机译:孟加拉国局部芒果品种检测使用卷积神经网络(CNN)模型的计算机视觉系统

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Magnifera Indica, traditionally known as mango, is a drupe found around the world in over 500 species. India has produced 19.5 million metric tons of mango in 2017. In Bangladesh, mango has been referred as the national tree and government has included endemic species of mango as geographical index (GI) of Bangladesh. Recognizing specific breeds has become a significant computer vision task. In this paper, we have proposed the convolutional neural network (CNN) based approach for detecting five mango species namely, Chosha, Fazli, Harivanga, Lengra and Rupali from 15000 different images. For better experimentation, we have applied three different models of CNN and analyzed the recognition rates with various criteria. For performance evaluation, we have utilized the classic metrics such as precision, recall, F1-score, ROC and accuracy. Among the experimented three models, the third model, outperformed in terms of accuracy with 92.80%.
机译:传统上称为芒果的Magnifera indica,是在500多种世界各地发现的芽孢杆菌。印度在2017年生产了1950万公吨芒果。在孟加拉国,芒果已被称为国家树和政府已将芒果的特点作为孟加拉国的地理指数(Gi)。识别特定品种已成为一项重要的计算机视觉任务。在本文中,我们提出了基于卷积神经网络(CNN)的方法来检测五种芒果种类,即来自15000种不同图像的Chosha,Fazli,Harivanga,Lengra和Rupali。为了更好的实验,我们已经应用了三种不同的CNN型号,并分析了各种标准的识别率。对于性能评估,我们利用了经典指标,如精度,召回,F1分数,ROC和精度。在实验三种模型中,第三种模型,在精度方面表现出92.80%。

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