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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.)

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.)

[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 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.)

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 Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology 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 Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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]

Copyright of Shipin Kexue/ Food Science is the property of Food Science 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 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 Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

[ 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]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology 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.)

【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.

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology 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.)

[Objective] Through the different data transformation of original spectral data, to analyze its correlativity between apple canopy PAR and establish the hyperspectral model of apple canopy PAR in the southern Xinjiang based on hyperspectral. the present paper aims to provide effective ways for a fast and accurate, no-damage, and wide range of timely, dynamic monitoring of vegetation PAR. [Method] Based on the different data transformation methods of the original spectral data, adapt correlation analysis and stepwise regression analysis. [Result]The canopy spectral reflectance with different transferred data has great correlations between apple canopy PAR, the correlation after differential transformation was higher than that of the original. After the accuracy evaluation, it is found in model that the original spectral data after the reciprocal first order differential transformation, the estimation model has the highest degree of fitting, and the first order differ­ential , logarithmic first order differential are following. [Conclusion ] The spectral data with different data transformation have certain correla­tion with apple canopy in Tarim Rasin, and it can use the data with differential, logarithmic differential, reciprocal differential transformation to establish estimated model of tarim basin apple canopy PAR. [ABSTRACT FROM AUTHOR]

Correctly identifying apple from image is the premise of apple harvesting robot for achiving automatic picking. On the basis of the study of this problem, this paper proposed a new apple target recognition algorithm based on significant contour. Firstly, it used the K-means unsupervised clustering algorithm to segment apple image which was divided into background and apple target area. Due to factors such as light, the apple target region after K-means segmenting contained large hole, so it introduced the ASIFT feature to match between the complete apple target image and the apple target image which included hole, and recorded the features corresponding to the hole, and filled the hole by pixels relating to those features. Then, based on the area, it detected the apple target image5 s contour by gPb contour detector to generate longer and moreobvious gray contour image. Finally, it removed much edge noise around the target through the dynamic threshold Otsu for automatic threshold processing to process the gray contour image, and achieved a complete extraction of the contour of the target by determining the continuous and significant contour. Experimental results show that, the proposed approach is more accurate and more robust, the correct recognition rate of apple target is above 98%. [ABSTRACT FROM AUTHOR]

Vision system is one of the most important parts of apple picking robot, which, to some extent, determines the quality and the speed of picking task implemented by apple picking robot. In this review, we enumerated the existing apple picking robots. Some information, such as type of visual sensors, hardware of vision system, success rate of harvesting and run time, was illustrated in details. Meanwhile, on the basis of discussing the vision system of apple picking robot, we focused on summarizing hardware structure of existing vision systems and apple image segmentation methods as well as apple recognition and localization methods applied in vision systems. The vision system of apple picking robot mainly includes machine vision system, laser vision system, three-dimensional vision system and vision system formed by machine vision and other vision system. And the machine vision system can be classified into 3 types according to the number of image sensors used, that is, monocular vision system, binocular vision system and multi vision system. The recognition and localization of apple target is the first step of the implementation of the picking task for picking robot. The currently used apple segmentation and recognition methods include threshold segmentation algorithm, chromatic aberration based algorithm, K-mean clustering algorithm, region growing algorithm, segmentation method combining 2 or more algorithms, and so on. There are 4 methods that are commonly used in the localization of apple target. They are the methods based on centroid, fitting circle, symmetry axes, and three-dimensional coordinates, respectively. In natural scene, the recognition and localization of apple target may be affected by many factors. Hence, recognition and localization of apple target under different conditions, such as color nonuniformity, different illumination, shadow on the surface, oscillation, overlapping and occlusion, was reviewed and analyzed. Among all these conditions, occlusion can be regarded as the most serious factor. The condition of occlusion can be roughly divided into 4 kinds, i.e. apple target blocked by other apple, by branches, by leaves and by branches, leaves and other apple simultaneously. As for apple targets blocked by branches, one apple may be separated by branches, thus causing that an apple may be recognized as several apples. For the apple targets blocked by leaves, the symmetry of apple can be utilized to localize apple targets. The apple targets blocked by other apple can be considered as overlapping. There are overlapped apples with series connection, parallel connection, and blend connection. Because of the complexity of overlapping, the recognition and localization of apple targets blocked by other apple target is a little more difficult. In addition, the detection of obstacles like tree trunk and branches in apple orchard is important for apple picking robots to avoid obstacles, and thus obstacles detection methods were summarized in this review. In the process of target recognition and localization, binocular vision technology was commonly used in vision system. The key point of binocular vision technology is stereo matching. Therefore, stereo matching was then reviewed, and the image matching methods in existence can generally be divided into 2 categories, i.e. region-based image matching method and feature-based image matching method. What's more, the problems exist in recognition and localization methods used in the vision system of apple picking robot, including accuracy, effectiveness, character of real-time and universal applicability, were analyzed. Further study will concentrate on optimizing the structure of vision system, optimizing the intelligent algorithms used in the vision system, improving the real-time capability, recognizing and locating apple targets when the apple targets and vision systems are influenced by oscillation, and improving cost performance. The paper has summarized and analyzed vision system of apple picking robot comprehensively, which can provide reference for future research. [ABSTRACT FROM AUTHOR]

The application of image recognition technology in apple fruit automatic picking and selecting process is an important way for the development of apple industry. At present, the apple image identification and detection take long time and its recognition rate is low. The paper analyzes the image denoising, feature analysis, feature extraction and other key issues, and focuses on occlusion of apple fruit recognition method. Based on the Otsu adaptive threshold segmentation method, an apple detection technology based on local maxima is proposed. Experimental results show7 that the method can accurately extract the characteristics of apple fruit compared with the traditional identification method and local maxima method, especially, for adjacent and occluded the apple image recognition, it maintains a high rate of accuracy. The device can greatly improve automatic level of picking robot for apple. [ABSTRACT FROM AUTHOR]

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 Food Research & Development is the property of Food Research & Development 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.)