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

机译:基于卷积神经网络(CNN)模型的孟加拉国本地芒果品种检测的计算机视觉系统

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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:n木(Magnifera Indica),传统上被称为芒果,是一种核果,在世界范围内发现有500多种。印度在2017年生产了1950万吨芒果。在孟加拉国,芒果被称为国家树,而政府也将芒果的特有物种纳入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,用于从15000种不同的图像中检测出5种芒果物种,即Chosha,Fazli,Harivanga,Lengra和Rupali。为了更好地进行实验,我们应用了三种不同的CNN模型,并使用各种标准对识别率进行了分析。对于性能评估,我们利用了经典指标,例如精度,召回率,F1得分,ROC和准确性。在实验的三个模型中,第三个模型的准确率超过了92.80%。

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