基于卷积神经网络的水果计算机分类

Rajesh Yamparala, Ramaiah Challa, V. Kantharao, P. Krishna
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引用次数: 13

摘要

如今,自动化在每个领域都很普遍。在农业生产中,对果实、叶片、土壤、气候条件进行分类是提高农业产量的必要条件。在这些水果的分类是非常重要的和具有挑战性的任务,因为许多水果看起来像颜色,形状,大小方面。它非常需要对水果病害进行计算机检测,因为早期检测可以保护整个作物免受损害。在这里,水果的分类已经成为检测水果病害的第一步。本文提出了基于卷积神经网络(CNN)的分类方法,与目前提出的其他方法相比,该方法的分类结果达到了90%。实验用200张水果图像的数据集进行,其中苹果水果图像为50张,芒果50张,橙子50张,剩下的50张是葡萄。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computerized Classification of Fruits using Convolution Neural Network
Now a days automation in every field becomes common. While coming to the agriculture field, it has become necessity for classification of fruits, leaves, soils, climatic conditions for better yielding of farming. Among these classification of fruits is very essential and challenging task as many fruits looks a like interms of colour, shape, size. It is very much needed for computerised detection of diseases in a fruits where early detection protects from damaging the entire crop. Here classification of fruits has become the first step in detection of fruits diseases. Here Convolution Neural Network(CNN) based classification method is proposed which gives a better classification result of 90% compared to other proposed methodologies till now. Experiments are held with the dataset of 200 images of fruits in which apple fruit images are 50, mango 50, orange 50 and the remaining 50 are grapes.
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