AgriEngineeringPub Date : 2024-02-05DOI: 10.3390/agriengineering6010019
Alfonso Montaño, Sofía Redondo-Redondo, Laura Moreno, Manuel Zambrano
{"title":"The Oil:Water Ratio in the Vertical Centrifuge Separator and Its Influence in Phenolic Compounds in the Virgin Olive Oil and the Olive Mill Wastewater (Alpechín)","authors":"Alfonso Montaño, Sofía Redondo-Redondo, Laura Moreno, Manuel Zambrano","doi":"10.3390/agriengineering6010019","DOIUrl":"https://doi.org/10.3390/agriengineering6010019","url":null,"abstract":"The use of the vertical centrifuge in the olive oil production process is generally assumed to be habitual and necessary for the elimination of both the vegetation water and the small olive pulp particles that are not eliminated during solid–liquid separation (horizontal centrifugation). Trials were carried out with different oil:water ratios to study the influence of this variable on both the quality parameters of the olive oils obtained and the loss of oil with the olive wastewater. The trials were carried out at the industrial mill level with oil:water ratios between 0.6 and 5.5. While no differences were observed in the quality parameters of the oils obtained, correct adjustment of the oil:water flow rates reduced the loss of phenols present in the oils by around 30%. In addition, the results show a direct relationship between the soluble effluent and the conductivity of the olive mill wastewater (alpechín) with the loss of oil in the effluent. This work proves that both oil quality and the competitiveness of the olive oil value chain can be increased with energy savings, water consumption reduction, and environmental sustainability.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"32 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804256","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}
AgriEngineeringPub Date : 2024-02-04DOI: 10.3390/agriengineering6010018
Dong Cong Trinh, Anh Tuan Mac, Khanh Giap Dang, Huong Thanh Nguyen, Hoc Thai Nguyen, Thanh Dang Bui
{"title":"Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection","authors":"Dong Cong Trinh, Anh Tuan Mac, Khanh Giap Dang, Huong Thanh Nguyen, Hoc Thai Nguyen, Thanh Dang Bui","doi":"10.3390/agriengineering6010018","DOIUrl":"https://doi.org/10.3390/agriengineering6010018","url":null,"abstract":"Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"2014 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139807160","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}
AgriEngineeringPub Date : 2024-02-04DOI: 10.3390/agriengineering6010018
Dong Cong Trinh, Anh Tuan Mac, Khanh Giap Dang, Huong Thanh Nguyen, Hoc Thai Nguyen, Thanh Dang Bui
{"title":"Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection","authors":"Dong Cong Trinh, Anh Tuan Mac, Khanh Giap Dang, Huong Thanh Nguyen, Hoc Thai Nguyen, Thanh Dang Bui","doi":"10.3390/agriengineering6010018","DOIUrl":"https://doi.org/10.3390/agriengineering6010018","url":null,"abstract":"Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"16 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139867120","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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