水果分类的深度学习架构

S. B., Senthil Prabha R, Ravitha Rajalakshmi N
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引用次数: 0

摘要

. 农业是成本要求高的领域之一,需要熟练的工人来收割。为了满足这一需求,需要使用机器人进行收获,这就需要精确的水果检测系统。机器人必须扫描图像并识别水果,这是一个至关重要的过程,因为识别系统面临着前所未有的挑战,如遮挡、变形、光照条件。本文的目标是通过解决图像识别中的这些挑战,建立一个准确可靠的水果识别系统。在水果识别系统中,利用深度学习算法卷积神经网络来识别图像的特征并对图像进行分类。该系统使用Fruit-360数据集进行评估,该数据集由60个不同类别的43329张图像组成。在该系统的辅助下,系统的可量化精度提高了约97%,系统的总损耗约为0.13。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Architecture For Fruit Classification
. The agricultural industries are one of the cost demanding fields placing a requirement on skilled laborers for harvesting. To meet the demands, robots are employed to harvest which mandates the need for accurate fruit detection system. The robot has to scan the image and recognize the fruit, which is the crucial process as the recognition system faces unprecedented challenges like occlusion, deformation, illumination conditions. The objective of this work is to build an accurate and reliable fruit recognition system by addressing these challenges in image recognition. Convolutional neural network, a deep learning algorithm is used to identify the features of an image and classify the image in the fruit recognition system. The system is evaluated with Fruit-360 dataset consisting of 43329 images of 60 different categories. With the aid of the proposed system, quantifiable improvement of about 97% accuracy is achieved and the total loss of the system is about 0.13.
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