Pub Date : 2023-03-01DOI: 10.1109/TAFE.2023.3265428
Priyabrata Karmakar;Shyh Wei Teng;Manzur Murshed;Paul Pang;Cuong Van Bui
Wheat is one of the major staple crops across the globe. Therefore, it is mandatory to measure, maintain, and improve the wheat quality for human consumption. Traditional wheat quality measurement methods are mostly invasive, destructive, and limited to small samples of wheat. In a typical supply chain of wheat, there are many receival points where bulk wheat arrives, gets stored, and then gets forwarded as per the requirements. In these receival points, the application of traditional quality measurement methods is difficult and often very expensive. Therefore, there is a need for non-invasive, non-destructive real-time methods for wheat quality assessments. One such method that fulfils the abovementioned criteria is hyperspectral imaging (HSI) for food quality measurement and it can also be applied to bulk samples. In this article, we have investigated how HSI has been used in literature for assessing stored wheat quality. So that the required information to implement real-time quality assessment methods at the different stages of the Australian supply chain can be made available in a single and compact document.
{"title":"A Guide to Employ Hyperspectral Imaging for Assessing Wheat Quality at Different Stages of Supply Chain in Australia: A Review","authors":"Priyabrata Karmakar;Shyh Wei Teng;Manzur Murshed;Paul Pang;Cuong Van Bui","doi":"10.1109/TAFE.2023.3265428","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3265428","url":null,"abstract":"Wheat is one of the major staple crops across the globe. Therefore, it is mandatory to measure, maintain, and improve the wheat quality for human consumption. Traditional wheat quality measurement methods are mostly invasive, destructive, and limited to small samples of wheat. In a typical supply chain of wheat, there are many receival points where bulk wheat arrives, gets stored, and then gets forwarded as per the requirements. In these receival points, the application of traditional quality measurement methods is difficult and often very expensive. Therefore, there is a need for non-invasive, non-destructive real-time methods for wheat quality assessments. One such method that fulfils the abovementioned criteria is hyperspectral imaging (HSI) for food quality measurement and it can also be applied to bulk samples. In this article, we have investigated how HSI has been used in literature for assessing stored wheat quality. So that the required information to implement real-time quality assessment methods at the different stages of the Australian supply chain can be made available in a single and compact document.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"29-40"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50425695","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}
Pub Date : 2023-02-10DOI: 10.1109/TAFE.2022.3232280
Yi-Bing Lin;Yun-Wei Lin;Ling-Han Kao
Electric energy prediction is an important issue and has been studied for many years. The prediction approaches have evolved from traditional statistical methods, conventional machine learning methods, deep learning (DL) methods, and then hybrid deep learning methods. This article proposes ElectricityTalk, an Internet of Things (IoT) platform for smart farms, which integrates the artificial intelligence (AI) mechanism with farming IoT devices for electric energy prediction and anomaly detection. The AI mechanism called AItalk is designed with modified convolution neural network (CNN) and long short-term memory models. Traditional electric energy prediction approaches only consider the information provided by smart meters. This article shows that with the extra IoT switch status information in the smart farm and postprocessing with a simple yet novel random walk model, the performance of ElectricityTalk is significantly improved (by 34.5%) as compared with the AI mechanism without the farming IoT switch information. We show that the mean absolute percentage error of AItalk is 8.62% (for the UCI dataset) and 1.53% (for the Bao farm dataset), which outperforms the previous solutions. We also show that ElectricityTalk detects all anomalies in real farm operations, and can achieve recall of 1 and precision larger than 0.994, which also outperforms the previous solutions. In particular, our mechanism can detect all anomalies in three minutes, which has not been reported in previous studies.
{"title":"Anomaly Detection for Electric Energy Consumption in Smart Farms","authors":"Yi-Bing Lin;Yun-Wei Lin;Ling-Han Kao","doi":"10.1109/TAFE.2022.3232280","DOIUrl":"https://doi.org/10.1109/TAFE.2022.3232280","url":null,"abstract":"Electric energy prediction is an important issue and has been studied for many years. The prediction approaches have evolved from traditional statistical methods, conventional machine learning methods, deep learning (DL) methods, and then hybrid deep learning methods. This article proposes ElectricityTalk, an Internet of Things (IoT) platform for smart farms, which integrates the artificial intelligence (AI) mechanism with farming IoT devices for electric energy prediction and anomaly detection. The AI mechanism called AItalk is designed with modified convolution neural network (CNN) and long short-term memory models. Traditional electric energy prediction approaches only consider the information provided by smart meters. This article shows that with the extra IoT switch status information in the smart farm and postprocessing with a simple yet novel random walk model, the performance of ElectricityTalk is significantly improved (by 34.5%) as compared with the AI mechanism without the farming IoT switch information. We show that the mean absolute percentage error of AItalk is 8.62% (for the UCI dataset) and 1.53% (for the Bao farm dataset), which outperforms the previous solutions. We also show that ElectricityTalk detects all anomalies in real farm operations, and can achieve recall of 1 and precision larger than 0.994, which also outperforms the previous solutions. In particular, our mechanism can detect all anomalies in three minutes, which has not been reported in previous studies.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"2-14"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50425694","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}
Pub Date : 2022-09-30DOI: 10.1109/TAFE.2022.3201675
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
潜在作者被要求提交新的、未发表的手稿，以纳入本论文征集中描述的即将到来的活动。
{"title":"Get Published in the IEEE Transactions on AgriFood Electronics (TAFE)","authors":"","doi":"10.1109/TAFE.2022.3201675","DOIUrl":"https://doi.org/10.1109/TAFE.2022.3201675","url":null,"abstract":"Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"58-58"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9787768/9906904/09906905.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50225508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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