Hassan Shabani Mputu;Ahmed-Abdel Mawgood;Atsushi Shimada;Mohammed S. Sayed
{"title":"使用混合 CNN-SVM 模型实时评估番茄质量","authors":"Hassan Shabani Mputu;Ahmed-Abdel Mawgood;Atsushi Shimada;Mohammed S. Sayed","doi":"10.1109/LES.2024.3370634","DOIUrl":null,"url":null,"abstract":"The current quality assessment for fruits and vegetables relies on subjective human judgment and manual inspection, resulting in inconsistencies and inefficiencies. Due to that, there is a need for a real-time system that can accurately and efficiently assess the quality of fruits and vegetables by analyzing various parameters, such as color, texture, size, and blemishes, to ensure consistency and reduce waste in the food supply chain. This study presents the development of a real-time tomato classification system using a hybrid model that combines convolutional neural network (CNN) and support vector machines (SVMs) deployed on the embedded single-board NVIDIA Jetson TX1. The selected CNN model EfficientNetB0 was used for feature extraction and SVM for classification. Notably, the EfficientNetB0-SVM hybrid model demonstrated impressive efficiency, achieving an average accuracy of 93.54% for classifying static tomato images stored in a board into healthy or reject with a testing time of 0.0216-s per image. Also, during real-time implementation, the proposed hybrid model attained an average inference speed of 15.6 frames per second (15.6 FPS), with an accuracy of 78.57% in classifying actual tomatoes into healthy or reject. The classification decision was taken based on 5 images for each tomato captured at different angles to ensure the detection of any blemishes from almost all sides of the tomato. The performance of the proposed model outperforms that of the state-of-the-art (SOTA) methods in accuracy, testing time per image, and real-time prediction accuracy.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"453-456"},"PeriodicalIF":1.7000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-Time Tomato Quality Assessment Using Hybrid CNN-SVM Model\",\"authors\":\"Hassan Shabani Mputu;Ahmed-Abdel Mawgood;Atsushi Shimada;Mohammed S. Sayed\",\"doi\":\"10.1109/LES.2024.3370634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The current quality assessment for fruits and vegetables relies on subjective human judgment and manual inspection, resulting in inconsistencies and inefficiencies. Due to that, there is a need for a real-time system that can accurately and efficiently assess the quality of fruits and vegetables by analyzing various parameters, such as color, texture, size, and blemishes, to ensure consistency and reduce waste in the food supply chain. This study presents the development of a real-time tomato classification system using a hybrid model that combines convolutional neural network (CNN) and support vector machines (SVMs) deployed on the embedded single-board NVIDIA Jetson TX1. The selected CNN model EfficientNetB0 was used for feature extraction and SVM for classification. Notably, the EfficientNetB0-SVM hybrid model demonstrated impressive efficiency, achieving an average accuracy of 93.54% for classifying static tomato images stored in a board into healthy or reject with a testing time of 0.0216-s per image. Also, during real-time implementation, the proposed hybrid model attained an average inference speed of 15.6 frames per second (15.6 FPS), with an accuracy of 78.57% in classifying actual tomatoes into healthy or reject. The classification decision was taken based on 5 images for each tomato captured at different angles to ensure the detection of any blemishes from almost all sides of the tomato. The performance of the proposed model outperforms that of the state-of-the-art (SOTA) methods in accuracy, testing time per image, and real-time prediction accuracy.\",\"PeriodicalId\":56143,\"journal\":{\"name\":\"IEEE Embedded Systems Letters\",\"volume\":\"16 4\",\"pages\":\"453-456\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Embedded Systems Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10445703/\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Embedded Systems Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10445703/","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Real-Time Tomato Quality Assessment Using Hybrid CNN-SVM Model
The current quality assessment for fruits and vegetables relies on subjective human judgment and manual inspection, resulting in inconsistencies and inefficiencies. Due to that, there is a need for a real-time system that can accurately and efficiently assess the quality of fruits and vegetables by analyzing various parameters, such as color, texture, size, and blemishes, to ensure consistency and reduce waste in the food supply chain. This study presents the development of a real-time tomato classification system using a hybrid model that combines convolutional neural network (CNN) and support vector machines (SVMs) deployed on the embedded single-board NVIDIA Jetson TX1. The selected CNN model EfficientNetB0 was used for feature extraction and SVM for classification. Notably, the EfficientNetB0-SVM hybrid model demonstrated impressive efficiency, achieving an average accuracy of 93.54% for classifying static tomato images stored in a board into healthy or reject with a testing time of 0.0216-s per image. Also, during real-time implementation, the proposed hybrid model attained an average inference speed of 15.6 frames per second (15.6 FPS), with an accuracy of 78.57% in classifying actual tomatoes into healthy or reject. The classification decision was taken based on 5 images for each tomato captured at different angles to ensure the detection of any blemishes from almost all sides of the tomato. The performance of the proposed model outperforms that of the state-of-the-art (SOTA) methods in accuracy, testing time per image, and real-time prediction accuracy.
期刊介绍:
The IEEE Embedded Systems Letters (ESL), provides a forum for rapid dissemination of latest technical advances in embedded systems and related areas in embedded software. The emphasis is on models, methods, and tools that ensure secure, correct, efficient and robust design of embedded systems and their applications.