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Comparison of Support Vector Machines and Deep Learning for Plant Classification in Smart Agriculture Applications 支持向量机与深度学习在智能农业植物分类中的应用比较
Signal Processing and Vision Pub Date : 2022-12-17 DOI: 10.5121/csit.2022.122202
Esmael Hamuda, Ashkan Parsi, M. Glavin, E. Jones
{"title":"Comparison of Support Vector Machines and Deep Learning for Plant Classification in Smart Agriculture Applications","authors":"Esmael Hamuda, Ashkan Parsi, M. Glavin, E. Jones","doi":"10.5121/csit.2022.122202","DOIUrl":"https://doi.org/10.5121/csit.2022.122202","url":null,"abstract":"In this paper, we investigate the use of deep learning approaches for plant classification (cauliflower and weeds) in smart agriculture applications. To perform this, five approaches were considered, two based on well-known deep learning architectures (AlexNet and GoogleNet), and three based on Support Vector Machine (SVM) classifiers with different feature sets (Bag of Words in L*a*b colour space, Bag of Words in HSV colour space, Bag of Words of Speeded-up Robust Features (SURF)). Two types of datasets were used in this study: one without Data Augmentation and the second one with Data Augmentation. Each algorithm's performance was tested with one data set similar to the training data, and a second data set acquired under challenging conditions such as various weather conditions, heavy weeds, and several weed species that have a similarity of colour and shape to the crops. Results show that the best overall performance was achieved by DL-based approaches.","PeriodicalId":153862,"journal":{"name":"Signal Processing and Vision","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122823868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Distributed Arithmetic Based Approach for the Realization of the Signed-Regressor LMS Adaptive Filter 基于分布式算法的有符号回归LMS自适应滤波器实现方法
Signal Processing and Vision Pub Date : 2022-12-17 DOI: 10.5121/csit.2022.122214
M. S. Prakash, R. Shaik
{"title":"A Distributed Arithmetic Based Approach for the Realization of the Signed-Regressor LMS Adaptive Filter","authors":"M. S. Prakash, R. Shaik","doi":"10.5121/csit.2022.122214","DOIUrl":"https://doi.org/10.5121/csit.2022.122214","url":null,"abstract":"This paper presents a distributed arithmetic (DA) based approach for the implementation of signedregressor LMS adaptive filter. DA, although is an efficient technique for the implementation of fixed coefficient filters, the adaptive filter implementation using DA is not a straight-forward task as the partialproducts of the filter weights have to be updated in every iteration. This is achieved by storing the partialproducts of the signum values of the input samples in a look-up-table (LUT). It has been shown that this LUT can be updated to accommodate the partial-products of newest set of samples in an efficient way using the circular- shifting of its address bits. Results indicate that the proposed filter can give better throughputs compared to multiply-and-accumulate (MAC) based implementation and can be effective when implementing large filters. With proper choice of system parameters, the proposed architecture for a 32- tap filter consumes around 87% less number of adder units while providing similar throughput performance compared to most recent existing DA based architecture.","PeriodicalId":153862,"journal":{"name":"Signal Processing and Vision","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133839865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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