Comparative study on fruit development and oil synthesis in two cultivars of Camellia oleifera

Zhang, Fanhang; Li, Ze; Zhou, Jinlin; Gu, Yu‐Cheng; Tan, Xiaofeng

doi:10.1186/s12870-021-03114-2

Cited by 34 publications

(33 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this phenomenon, we can determine that when identifying oil tea cultivars, the CBAM module can not only determine the location of key information but also As shown in Figure 7, it is apparent that when identifying the cultivars of oil tea, EfficientNet-B4 focused on the areas of the fruit and background, whereas EfficientNet-B4-CBAM paid substantial attention to the areas of the fruit, but paid little attention to the background areas. According to the experience of human experts, when identifying oil tea cultivars, the fruit areas of oil tea images can often provide key information for cultivar identification, whereas the background areas of the oil tea image generally interfere with the cultivar identification [52].…”

Section: Visual Analysis Of Cultivar Identification Resultsmentioning

confidence: 99%

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Zhu

Zhang

Sun

et al. 2021

Forests

View full text Add to dashboard Cite

Cultivar identification is a basic task in oil tea (Camellia oleifera C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.

show abstract

Section: Visual Analysis Of Cultivar Identification Resultsmentioning

confidence: 99%

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Zhu

Zhang

Sun

et al. 2021

Forests

View full text Add to dashboard Cite

show abstract

“…RNA sequencing data were retrieved from previous studies by our research group [ 64 ], with accession number: PRJNA 693152.…”

Section: Methodsmentioning

confidence: 99%

Whole-Genome Identification and Analysis of Multiple Gene Families Reveal Candidate Genes for Theasaponin Biosynthesis in Camellia oleifera

Yang

Zhou

et al. 2022

IJMS

Self Cite

View full text Add to dashboard Cite

Camellia oleifera is an economically important oilseed tree. Seed meals of C. oleifera have a long history of use as biocontrol agents in shrimp farming and as cleaning agents in peoples’ daily lives due to the presence of theasaponins, the triterpene saponins from the genus Camellia. To characterize the biosynthetic pathway of theasaponins in C. oleifera, members of gene families involved in triterpenoid biosynthetic pathways were identified and subjected to phylogenetic analysis with corresponding members in Arabidopsis thaliana, Camellia sinensis, Actinidia chinensis, Panax ginseng, and Medicago truncatula. In total, 143 triterpenoid backbone biosynthetic genes, 1169 CYP450s, and 1019 UGTs were identified in C. oleifera. The expression profiles of triterpenoid backbone biosynthetic genes were analyzed in different tissue and seed developmental stages of C. oleifera. The results suggested that MVA is the main pathway for triterpenoid backbone biosynthesis. Moreover, the candidate genes for theasaponin biosynthesis were identified by WGCNA and qRT-PCR analysis; these included 11 CYP450s, 14 UGTs, and eight transcription factors. Our results provide valuable information for further research investigating the biosynthetic and regulatory network of theasaponins.

show abstract

“…The transverse and vertical diameters of each Camellia oleifera fruit sample were determined on a digital caliper (Deguqmnt, MNT-150T, Hanover, Germany). To acquire the dry seeds, the fresh seeds were deactivated at 105 °C for 15 min and baked at 70 °C for 72 h [ 13 ]. Similarly, the fresh seed weight ( W f ) and dry seed weight ( W d ) of each Camellia oleifera fruit sample were determined with an electronic balance (Shimadzu, UW2200H, Kyoto, Japan).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the yield and quality of Camellia oil are determined by the maturity stage of the harvested fruit. As a rule of thumb, both early and later-harvested Camellia oleifera fruits exhibit decreased oil content and quality [ 13 , 14 ]. Hence, accurate identification of Camellia oleifera fruit maturity has to be made.…”

Section: Introductionmentioning

confidence: 99%

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Zhu

Shen

Wang

et al. 2022

Foods

View full text Add to dashboard Cite

Maturity grading and identification of Camellia oleifera are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in Camellia oleifera production and research is the worldwide confusion regarding the grading and identification of Camellia oleifera fruit maturity. To solve this problem, a Camellia oleifera fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 Camellia oleifera fruit samples and 2628 Camellia oleifera fruit digital images collected using a smartphone. The proposed model for grading Camellia oleifera fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 Camellia oleifera fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for Camellia oleifera fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for Camellia oleifera fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of Camellia oleifera fruits, which is especially critical for the improvement of Camellia oil production and quality.

show abstract

Comparative study on fruit development and oil synthesis in two cultivars of Camellia oleifera

Cited by 34 publications

References 56 publications

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Whole-Genome Identification and Analysis of Multiple Gene Families Reveal Candidate Genes for Theasaponin Biosynthesis in Camellia oleifera

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Contact Info

Product

Resources

About