{"title":"Distinction of Edible and Inedible Harvests Using a Fine-Tuning-Based Deep Learning System","authors":"Shinji Kawakura, R. Shibasaki","doi":"10.18178/joaat.6.4.236-240","DOIUrl":"https://doi.org/10.18178/joaat.6.4.236-240","url":null,"abstract":"—Effectively detecting and removing inedible harvests before or after harvesting is important for many agri-workers. Recent studies have suggested diverse measures, including various robot arm-based machines for harvesting vegetables and pulling up weeds, using camera systems to detect relevant coordinates. Although some of these systems have included monitoring and identification tools for edible and inedible targets, their accuracy has not been sufficient for use. Thus, further improvements have incorporated computing into the process based on human feelings and commonsense-based thinking, which considers up-to-date technologies and determines how solutions reflect the experience of traditional agri-workers. Our focus is on Japanese small- to middle-sized farms. Thus, we developed a fine-tuning (transfer-learning)-based deep learning system that gathers field pictures and performs static visual data analyses using artificial intelligence (AI)-based computing. In this study, pictures included kiwi fruits, eggplants, and mini tomatoes in outdoor farmlands. We focused on several program-based applications with deep learning-based systems using several hidden layers. To align with this year’s technical trends, the data is presented concerning two patterns with different target layers: (1) all bonding layers with a revised pattern, and (2) some convolution layers with a visual geometry group (VGG) 16 and picture classifier created by convolutional neural network (CNN) revised pattern. Our results confirmed the utility of the fine-tuning methodologies, thus supporting other similar analyses in different academic research fields. In future, these results could assist the development of automatic agricultural harvesting systems and other high-tech agri-systems.","PeriodicalId":222254,"journal":{"name":"Journal of Advanced Agricultural Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129837722","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}
{"title":"Evaluation of Uncertainty of Trichloromethane Extract","authors":"Cheng-guo Shen, Fengrun Guo, Wanru Zhou","doi":"10.18178/joaat.9.1.31-34","DOIUrl":"https://doi.org/10.18178/joaat.9.1.31-34","url":null,"abstract":"— The overall migration of pulp molding tableware in its food simulants often exceeds the prescribed limit. When that happens, the chloroform extraction experiment should be conducted to determine the residual content. In this paper, the overall migration of pulp molding tableware in the food simulant of 4% (volume fraction) acetic acid and the residual mass of chloroform extraction were tested. The influencing factors of the uncertainty were analyzed, the mathematical model of the uncertainty amount of the total migration amount was established by the experimental method, and the uncertainty components were calculated. The experimental results showed that the expanded uncertainty was 0.278mg/dm 2 (k=2), and the determination result after chloroform extraction was (13.25±0.278) mg/dm 2 .","PeriodicalId":222254,"journal":{"name":"Journal of Advanced Agricultural Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191260","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}
Wan Lin, Kuei-Tso Lee, Sheng-Jyh Wang, M. Lai, Po-Hsun Chen
{"title":"A Monitoring and Forewarning System for Rice Planthoppers","authors":"Wan Lin, Kuei-Tso Lee, Sheng-Jyh Wang, M. Lai, Po-Hsun Chen","doi":"10.18178/joaat.6.3.180-186","DOIUrl":"https://doi.org/10.18178/joaat.6.3.180-186","url":null,"abstract":"—We develop a monitoring and forewarning system to detect planthoppers in paddy fields. Our detection algorithm consists of two stages. At the first stage, we extract the main paddy in the middle of an image by some traditional image processing techniques. At the second stage, we use a convolutional neural network to detect planthoppers within the extracted region. Our detection model is revised from the Single Shot MultiBox Detector (SSD). The original SSD model usually misrecognizes reflected light as planthoppers since a lot of background information has been discarded in the max pooling layers of the SSD model. To solve this misrecognition problem, we propose a new kind of pooling-- Local Difference Pooling. This proposed method greatly improves the performance of planthopper detection to achieve 89.38% precision and 91.93% recall.","PeriodicalId":222254,"journal":{"name":"Journal of Advanced Agricultural Technologies","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125757955","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}
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