Research on identification method of tangerine peel year based on deep learning

CHU, Ziyi; Li, Fengmei; WANG, Dongwei; Xu, Shusheng; GAO, Chunfeng; Bai, Haoran

doi:10.1590/fst.64722

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…, Mahamudul Hashan et al (2022 proposed a multilayer convolutional neural network MCNN to classify three apple leaf diseases, and the experimental results showed that the model achieved an accuracy of 98.4%. In 2022, Chu et al (2022) proposed a Chenpi-year recognition method based on deep learning. They used data-enhanced dataset and an improved ResNet50 model to accurately identify the year of tangerine peels.…”

Section: An Apple Leaf Disease Identification Model For Safeguarding ...mentioning

confidence: 99%

“…With the continuous development of technology, the ability and intelligence of computers to process information are also improving, and neural network technology has also been greatly developed. Neural network technology has been used in different fields, such as food technology (Xu et al, 2022) and automatic control (Bai et al, 2022). Krizhevsky et al (2017) proposed AlexNet in 2012 and won the ImageNet classification competition.…”

Section: Introductionmentioning

confidence: 99%

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An apple leaf disease identification model for safeguarding apple food safety

LIU

Bai

et al. 2023

Food Sci. Technol

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Apples are the fourth most produced fruit in the world, so it is important to safeguard them from disease damage. Although there are many deep learning-based plant disease detection models, the existing deep learning networks have complex structures and require large amounts of computational resources for support. Lightweight networks such as MobileNet and ShuffleNet designed for mobile devices could achieve better classification and faster recognition on public datasets, but their accuracy rates are not yet up to the requirements of practical applications. To address these problems, we constructed an improved apple leaf disease recognition algorithm based on MobileNetV2. We used feature reuse to improve the network structure, added a dense connection structure to the inverse residual module, and an ECA-Net attention module to increase the model's focus on diseased regions. We trained the improved model on the network on a dataset expanded by a generative adversarial network. The results showed that the improved model had a smaller number of model parameters, only 3.3 M, and a higher accuracy rate of 96.23% compared to Resnet50, ShuffleNet, and MobileNet models. The improved model had only 0.34 M addition in the number of parameters compared with MobileNet-V2, and had a 2.2% improvement in accuracy.

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Section: An Apple Leaf Disease Identification Model For Safeguarding ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An apple leaf disease identification model for safeguarding apple food safety

LIU

Bai

et al. 2023

Food Sci. Technol

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“…By designing different weight layers, neural network models with different depths can be established, such as VGG (Simonyan & Zisserman, 2014), GoogleNet (Szegedy et al, 2015) and ResNet (Chu et al, 2022;He et al, 2016). Although deeper networks may achieve higher accuracy, the training and inference speed of the model will decrease.…”

Section: R-cnn Backbonementioning

confidence: 99%

Boosted Mask R-CNN algorithm for accurately detecting strawberry plant canopies in the fields from low-altitude drone images

et al. 2022

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The research constructs a novel structure by integrating two parts of online object detection pipelines into the current state-ofthe-art Mask R-CNN algorithm to improve the detection performance. The DJI Mavic Air drone is used to collect low-altitude sensing images of strawberry plant canopies. The data augmentation method is employed to feed more instances into the original image dataset to boost the generalization and robustness of the detection model in the training procedure. A ResNet50 backbone combined with a feature pyramid network is presented to extract the features of strawberry plant canopies. The online hard example mining algorithm is introduced to mine hard samples to learn rich features and update model weights. Soft nonmaximum suppression based on recursive application on the remaining detection boxes within the predefined overlap threshold is proposed to improve the performance of identifying the large complex overlapping area of strawberry plant canopies. The qualitative results demonstrate that the improved detection model had an AP50 of 96.9 and an AR of 78.5 on the test set, which are approximately 30% higher than the original values.

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“…9 Chu et al proposed a new lightweight convolutional neural network model for identifying Pericarpium Citri Reticulatae images captured by industrial cameras, which can also accurately and non-destructive identify the storage age of Pericarpium Citri Reticulatae. 10 However, traditional NIR spectrometers can only obtain spectral information of samples, and traditional industrial cameras can only obtain spatial information. The features they extract are incomplete, which always leads to a low classification accuracy of Pericarpium Citri Reticulatae.…”

Section: Introductionmentioning

confidence: 99%

On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging

Guo,

Yu,

Hong

et al. 2023

Journal of Near Infrared Spectroscopy

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Pericarpium Citri Reticulatae is a traditional Chinese medicine with high medicinal value, and its storage age has a great impact on its ethno-pharmaceutical relevance. At present, there is a situation in the market place where Pericarpium Citri Reticulatae with short storage age is fraudulently sold as Pericarpium Citri Reticulatae with long storage age, and some unaged orange peels dyed with tea are sold as Pericarpium Citri Reticulatae at a high price. In this study, a rapid, on-site method for identifying the storage age of Xinhui Pericarpium Citri Reticulatae based on spectral imaging technology was described. The image features and spectral features were extracted respectively from the surface reflection spectral images of Pericarpium Citri Reticulatae, and a machine learning model was established to identify the storage age. This study explored the classification effect of the combination of different spectral pre-processing methods and machine learning models, and finally selected the combination of standard normal variate (SNV) and random forest (RF) models, to achieve 95% accuracy on the test dataset, showing excellent generalization performance. The result shows that the spectral imaging technology can rapidly identify the storage age of Xinhui Pericarpium Citri Reticulatae in real time, which has a great application prospect in the detection of the properties of medicinal materials.

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Research on identification method of tangerine peel year based on deep learning

Cited by 6 publications

References 23 publications

An apple leaf disease identification model for safeguarding apple food safety

An apple leaf disease identification model for safeguarding apple food safety

Boosted Mask R-CNN algorithm for accurately detecting strawberry plant canopies in the fields from low-altitude drone images

On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging

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