دورية أكاديمية

An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network

التفاصيل البيبلوغرافية
العنوان: An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network
المؤلفون: Yue Yuan, Jichi Chen, Kemal Polat, Adi Alhudhaif
المصدر: Current Research in Food Science, Vol 8, Iss , Pp 100723- (2024)
بيانات النشر: Elsevier, 2024.
سنة النشر: 2024
المجموعة: LCC:Nutrition. Foods and food supply
LCC:Food processing and manufacture
مصطلحات موضوعية: Fruit and vegetable freshness detection, CNN, BiLSTM, Model fusion, Parameter optimization, Nutrition. Foods and food supply, TX341-641, Food processing and manufacture, TP368-456
الوصف: Fruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combined with machine learning. However, manual extraction of features has the problem of poor adaptability, resulting in low efficiency in fruit and vegetable freshness detection. Although exist some studies that have introduced deep learning methods to automatically learn deep features that characterize the freshness of fruits and vegetables to cope with the diversity and variability in complex scenes. However, the performance of these studies on fruit and vegetable freshness detection needs to be further improved. Based on this, this paper proposes a novel method that fusion of different deep learning models to extract the features of fruit and vegetable images and the correlation between various areas in the image, so as to detect the freshness of fruits and vegetables more objectively and accurately. First, the image size in the dataset is resized to meet the input requirements of the deep learning model. Then, deep features characterizing the freshness of fruits and vegetables are extracted by the fused deep learning model. Finally, the parameters of the fusion model were optimized based on the detection performance of the fused deep learning model, and the performance of fruit and vegetable freshness detection was evaluated. Experimental results show that the CNN_BiLSTM deep learning model, which fusion convolutional neural network (CNN) and bidirectional long-short term memory neural network (BiLSTM), is combined with parameter optimization processing to achieve an accuracy of 97.76% in detecting the freshness of fruits and vegetables. The research results show that this method is promising to improve the performance of fruit and vegetable freshness detection.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2665-9271
Relation: http://www.sciencedirect.com/science/article/pii/S2665927124000492; https://doaj.org/toc/2665-9271
DOI: 10.1016/j.crfs.2024.100723
URL الوصول: https://doaj.org/article/19ef3eb630fa4cb18d5bd0752868d72f
رقم الأكسشن: edsdoj.19ef3eb630fa4cb18d5bd0752868d72f
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:26659271
DOI:10.1016/j.crfs.2024.100723