果蔬新鲜度检测可以提高农产品管理效率,减少资源浪费和经济损失,在提高果蔬农产品附加值方面发挥着至关重要的作用。目前,果蔬新鲜度的检测主要依靠人工特征提取结合机器学习。然而,人工提取特征存在适应性差的问题,导致水果和蔬菜新鲜度检测效率低。尽管有一些研究引入了深度学习方法来自动学习表征水果和蔬菜新鲜度的深度特征,以应对复杂场景中的多样性和可变性。然而,这些果蔬新鲜度检测研究的性能有待进一步提高。基于此,本文提出了一种融合不同深度学习模型提取果蔬图像特征及图像中各区域间相关性的新方法,从而更客观准确地检测水果和蔬菜的新鲜度。首先,根据深度学习模型的输入要求,调整数据集中的图像大小。然后,融合深度学习模型提取了表征果蔬新鲜度的深层特征。最后,基于融合的深度学习模型的检测性能,优化融合模型的参数,并对果蔬新鲜度检测性能进行了评价。实验结果表明,CNN_BiLSTM深度学习模型,其中融合了卷积神经网络(CNN)和双向长短期记忆神经网络(BiLSTM),结合参数优化处理,在果蔬新鲜度检测中的准确率达到97.76%。研究结果表明,该方法有望提高果蔬新鲜度检测的性能。
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.