Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Yang, Baohua; Gao, Yuan; Yan, Qian; Qi, Lin; Zhu, Yue; Wang, Bing

doi:10.3390/s20185021

Cited by 29 publications

(14 citation statements)

References 41 publications

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“…The results showed that SAE-PLSR model had the best prediction results, r and root mean square error (RMSE) of prediction set were 0.938 and 0.654 respectively. With the improvement of deep learning theory, Yang et al introduced deep learning theory into the prediction of SSC content of peach, and proposed a SSC estimation method of fresh peach based on deep features of hyperspectral image fusion information (Yang et al, 2020). The distance from the peach sample to the lens is 220 mm, the wavelength range is 900-1740 nm, and the spectral resolution is 5 nm.…”

Section: Fruit Maturity and Biochemical Parameters Detection Based On...mentioning

confidence: 99%

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Huang

et al. 2023

Front. Plant Sci.

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Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.

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Section: Fruit Maturity and Biochemical Parameters Detection Based On...mentioning

confidence: 99%

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Huang

et al. 2023

Front. Plant Sci.

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“…Another significant factor, where HSI systems proved not only to detect nonobvious bruises of fruits but also capable of assessing internal quality parameters such as soluble solid content, firmness, pH value, antioxidant, etc. [161][162][163][164][165][166][167][168][169][170][171].…”

Section: Illustrated Traditional Measuring Technique (A) and (B) Nir-hsi (1) Single Band At 1450 Nm (2) Binary Image Obtained Indicating mentioning

confidence: 99%

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Eissa¹,

Helyes²,

Romano³

et al. 2022

A Glance at Food Processing Applications

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In recent decades, the quality and safety of fruits, vegetables, cereals, meats, milk, and their derivatives from processed foods have become a serious issue for consumers in developed as well as developing countries. Undoubtedly, the traditional methods of inspecting and ensuring quality that depends on the human factor, some mechanical and chemical methods, have proven beyond any doubt their inability to achieve food quality and safety, and thus a failure to achieve food security. With growing attention on human health, the standards of food safety and quality are continuously being improved through advanced technology applications that depend on artificial intelligence tools to monitor the quality and safety of food. One of the most important of these applications is imaging technology. A brief discussion in this chapter on the utilize of multiple imaging systems based on all different bands of the electromagnetic spectrum as a principal source of various imaging systems. As well as methods of analyzing and reading images to build intelligence and non-destructive systems for monitoring and measuring the quality of foods.

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“…Xu extracted the deep features of the spectrum to improve the detection of different types of Pu’er tea [ 27 ]. Yang et al designed different structures of stacked autoencoder (SAE) to extract the deep features of hyperspectral images, which improved the accuracy of the estimation of soluble solid content in peach [ 28 ]. The research mentioned above showed that deep features could effectively improve the classification, recognition, and prediction of target objects.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery

Yang

Yao

et al. 2021

Sensors

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Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

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Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Cited by 29 publications

References 41 publications

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery

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