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Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mycosystema is the property of Mycosystema Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objectives]This paper aimed to solve the problem of low target detection accuracy of apples with different ripeness under natural environment. [Methods]An improved YOLOv5s model SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block)was proposed for different ripeness apple detection. Firstly, the multi-scale target detection layer of YOLOv5s was improved, and the detection head of 160×160 feature map was constructed in Prediction to improve the detection accuracy of small-size apples with different ripeness. Secondly, Swin Transformer Block was integrated into the Backbone structure to strengthen the texture feature fusion of apples at the same level of ripeness, weaken the negative impact of the difference in texture feature distribution, and improve the generalization ability of the model. Finally, the Conv module of Neck structure was replaced by the dynamic convolution module ODConv, and the local feature mapping was refined to realize the full extraction of local apple fine-grained features. Experiments were carried out based on different ripeness apple data sets to verify the performance of the improved model. [Results]The precision, recall and average precision of the improved model SODSTR-YOLOv5s were 89.1%, 95.5% and 93.6%, respectively. The average precision of high, medium and low ripeness apples was 94.1%, 93.1% and 93.7%, respectively. The average detection time was 16 ms and the number of parameters was 7.34 M. Compared with the YOLOv5s model, the precision, recall and average precision of the improved model SODSTR-YOLOv5s increased by 3.8%, 5.0% and 2.9%, respectively, and the number of parameters and average detection time increased by 0.32 M and 5 ms, respectively. [Conclusions]The improved model SODSTR-YOLOv5s improved the detection ability of apples with different ripeness under natural environment. It can better meet the detection requirements of actual apple picking. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Agricultural Sciences is the property of South China Agricultural University, Guangdong Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Apple pesticide residues not only compromise food safety, but also seriously endanger public health. Accurate and rapid detection of pesticide residues is an important means to ensure food safety. This paper reviewed the detection methods of pesticide residues in apple in recent years and introduced traditional detection techniques such as gas chromatography, gas chromatography-mass spectrometry, high performance liquid chromatography, and high performance liquid chromatography-mass spectrometry, as well as rapid detection techniques including surface enhanced Raman spectroscopy, near infrared spectroscopy, electrochemical method, and several new detectable techniques in the application of pesticide residues detection in apple. Also it look forward to the combination of traditional detection techniques and rapid detection techniques to solve the problem in order to provide technical support for the detection of pesticide residues in apple. [ABSTRACT FROM AUTHOR]

[ Objective] This paper aimed to understand the biological information and function of the Heat shock transcription factor( Hsf) in apple. [ Method]36 Hsfs were retrieved from the apple genome GDH13 vl. l,confirmed by Pfam and NCBI Conserved Domain Database,and then,TBTools,ExPASy,MEME and other software were used to further analyze33 genes with complete Hsf domain. [ Result]The proteins encoded by these genes contained 189 - 530 amino acids, with molecular weights of 21 703. 71 - 58 972. 96, and isoelectric points of 4. 55 - 8. 58. The prediction of subcellular localization showed that MD00G1095900 and MD02G1171800 could be located in chloroplast/nucleus, MD05G1313700 could be located in nucleus/mitochondria,and other Hsf were localized in the nucleus. Chromosome mapping and synteny analysis showed that there was no tandem duplication of apple Hif genes, but there were segmental duplication in 16 pairs of 24 apple Hif genes. Under saline-alkali stress, the expression of most Hif genes were significantly down-regulated. Protein interaction network analysis showed that MD03 G1258300 interacts with heat shock protein, superoxide dismutase copper chaperone, protein phosphatase 2C, and sucrose unfermentable 1-associated protein kinase 'Y regulatory subunit. MD15G1283700 interacts with heat shock protein, IAA amino acid hydrolase and alkaline leucine zipper. [Conclusion] Hsfs of apple may play an important role in regulating the response to saline-alkali stress, which provides some theoretical reference for further study of biological functions of Hsfs in apple. [ABSTRACT FROM AUTHOR]

【Objective】By studying the hyperspectral characteristics of apple damage and establishing the best classification model of apple damaged area based on hyperspectral imaging, we can provide an important basis for realtime, rapid and accurate identification of apple damage.【Method】This paper investigates the use of hyperspectral image technology to detect mechanical damage on fruit surfaces using apple samples collected from Pinggu District, Beijing as the research object. Using the hyperspectral image (HSI) data in the range of 390-1 000 nm, the spectral response characteristics of the damaged and normal ROI areas was analysed by ratio spectroscopy, and three types of spectral indexes, namely the normalized difference spectral index (NDSI), the ratio spectral index (RSI) and the difference spectral index (DSI) were selected and constructed to better highlight the characteristics of the damaged areas..On the basis of these indexes, the spectral indexes with better ability to extract damage areas were selected, and the unsupervised data clustering algorithm of iterative self-organizing data analysis techniques algorithm (ISODATA) was used to extract apple damage areas.【Result】The spectral reflectance changed significantly when the apple surface was damaged. After waveband optimization, it was found that the reflectance of 528 nm and 676 nm could effectively identify the abnormal areas. Based on the selected feature bands, the recognition spectral indexes for apple damage detection were constructed, including NDSI, RSI and DSI. By the pixel value analysis of the spectral index images, the damaged area features were distinguished from the normal area features in each SI enhanced image obviously. Among them, the difference between the NDSI pixel averages of the two types of image features was the largest, reaching 0.629, which indicated that the established NDSI has a strong ability to distinguish between the damaged area and normal area features. Using the unsupervised classification method ISODATA classification, it was verified that the spectral feature index has high specificity in detecting apple damage, and the accuracy rate of apple damage reached 92.50%.【Conclusion】The results are applicable to the real-time and rapid detection of apple damage, providing a technical basis and reliable reference for the accurate management of apple production.