基于CNN模型的绿叶蔬菜识别与分类

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2023-08-03 DOI:10.1109/icABCD59051.2023.10220482

Eneia Filipe Vilanculos, T. Shongwe, Ali N. Hasan

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引用次数: 0

摘要

在大农场里，识别和分类蔬菜是一项挑战，尤其是当蔬菜的颜色和形状相似时。人工识别蔬菜需要时间，而且容易出错。因此，精确农业的自动分类过程，越来越多地利用图像处理和模式识别来识别水果和蔬菜，成为大农场蔬菜识别和分类的必要条件。本文采用CNN、MobileNet、VGG-16、Inception V3和ResNet 50五种不同的深度学习模型，对一种颜色和形状相似的绿叶蔬菜自动识别分类系统进行了评价。这些模型的精度在67%到99%之间。精度最高的模型是MobileNet。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Identification and classification of Green Leafy Vegetables using CNN models

Identifying and classifying vegetables in big farms is a challenge, especially when the vegetables are similar in colour and shape. Manual identification of vegetables takes time and is prone to errors. Therefore, the automatic classification process of the precision farming, increasingly using image processing and pattern recognition to identify fruits and vegetable, is becoming essential to identify and classify vegetables in big farms. In this paper, an automatic system for the identification and classification of green leafy vegetables, similar in colour and shape was evaluataed using five different deep learning models such as CNN, MobileNet, VGG-16, Inception V3 and ResNet 50. The accuracies of these models achieved in this paper vary from 67% to 99%. The model with the highest accuracy is the MobileNet.

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.