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Manual measurement on mango fruit dimensions is often confined to the time inefficiency and low accuracy. Specifically, the human judgment and simplified definitions of fruit shape can impact the morphology classification and subsequent packaging. Consequently, it is highly required for the dedicated platform to determine the morphological indicators for the accurate identification of mango fruit shapes. Such a platform can be expected to streamline the sizing process for the shape identification. Advanced techniques, such as three-dimensional (3D) scanning, have been successfully applied into the morphological analysis of other fruit types, demonstrating their potential to the high accuracy. Additionally, machine learning can also be integrated to significantly improve the classification of fruit shapes, in order to effectively mitigate the errors associated with human assessment. In this study, a 3D scanning platform was implemented to rapidly capture the geometric data of mango fruits. An accurate 3D model was constructed to facilitate the measurement on the morphological indices and shape identification. 75 mango varieties were selected with three replicates for each variety. 10 quantitative parameters were then measured, including volume, longitudinal and transverse diameters, as well as fruit shape indices using 3D scanning. A comparison was then made on the scanning and manual measurement. The results indicated that the precision of scanning was surpassed the manual ones, indicating the superiority of the former. Subsequently, eigenvalue, principal component, correlation and cluster analysis were conducted to categorize the mango varieties, according to their distinct fruit shapes. There were the rich genetic diversity and variability in the mango germplasm resources. Notably, the fruit shape index was taken as the critical indicators, together with longitudinal and transverse diameter. The comprehensive evaluation of fruit morphology was performed for the subsequent application of machine learning. The input parameters were then optimized to predict the modeling. A back-propagation (BP) neural network model was developed to classify the fruit shape, according to the SPSSPRO online data analysis platform. The architecture of model consisted of 100 hidden layers, with a learning rate set at 0.1 and a total of 1000 iterations. Furthermore, an success rate was achieved in the 94.12% accuracy, when testing on 68 samples and training with 157 samples. Moreover, the training set exhibited a precision and recall rate of 0.941, with a remarkably rapid recognition time of only 0.451 s. In conclusion, 3D point cloud data was utilized to provide the rapid, precise, and lossless morphological information for mango fruits. 3D point cloud maps were generated to further enhance the overall accuracy, such as fruit color information. Compared with the conventional, this approach can offer numerous advantages: rapid sampling, high accuracy, reduced susceptibility to external factors, and a non-contact measurement. A significant framework can also advance the field of fruit morphology analysis for future application in fruit classification and assessment. [ABSTRACT FROM AUTHOR]

The quality traits of 36 local pepper germplasm resources in Shandong were analyzed by variance analysis, coefficient of variation analysis, membership analysis and cluster analysis of different fruit shapes, regions and cluster categories. The results showed that the variation coefficient of quality traits among different pepper germplasm resources ranged from 14. 37% to 110. 26%, and the variation coefficient of capsaicin content was the largest(110. 26%). In different fruit shapes, the average value of crude fiber content was significantly different between groups(P = 0. 013∗ ). The coefficient of variation of linear pepper was the largest(165. 22%). The average membership degree of the five quality indexes was between 0. 18 and 0. 65, and the comprehensive quality was quite different. The average membership degree was more than 0. 60, and the top three pepper germplasm resources were ranked from large to small as follows : L949(0. 65)= L958(0. 65) > L951 (0. 64)> L933(0. 61)= L945(0. 61)= LP220(0. 61);the six pepper germplasm materials have good quality traits and can be used in medicinal, feeding and food processing industries. [ABSTRACT FROM AUTHOR]

In order to provide theoretical basis and research basis for the promotion of main varieties of Camellia oleifera in Xinyang, the fruit quality of 6 kinds of “Changlin series” Camellia oleifera that introduced to Xinyang, Henan province was compared, and the comprehensive performance of Camellia oleifera varieties were screened. Seventeen main characters of mature fruits（transverse diameter, longitudinal diameter, single fruit weight, peel thickness, seed number, seed weight, kernel oil content, palmitic acid, stearic acid, oleic acid, linoleic acid, α-linolenic acid, arachidonic acid, total phenol, vitamin E, squalene, β-sitosterol） were analyzed. The results showed that the fruit traits of ‘Changlin 27’ were stable, and all the fruit traits were in the forefront of the tested varieties. ‘Changlin 23’ had the highest oil content of 48.67%, while ‘Changlin 20’ had the lowest oil content of 40.74%. The highest oleic acid content of ‘Changlin 23’ was 83.05%. By comprehensive comparison, the content of secondary metabolites in oil of ‘Changlin 27’ tea seeds was obviously superior to other varieties. According to the correlation analysis of oil content, fatty acid content and functional components of Camellia oleifera seeds, it was concluded that ‘Changlin 27’ had stable quality, high oil content, balanced oil components and high content of secondary metabolites. Its comprehensive characters were significantly better than the other five varieties, and it could be used as the main variety to promote planting in Xinyang area. [ABSTRACT FROM AUTHOR]

[Objective] To provide theoretical basis for developing a new tomato breeding evaluation system, the endophytic bacterial communities structure in stems of tomato varieties with different fruit shapes were analyzed, the correlation between endophytic bacteria and fruit shape was explored. [ Method] B ioinformatics analysis of the endophytic bacterial community structure and related metabolic functions in stems of different fruit shapes tomatoes ( ellipsoid, flat, long and round) were analyzed using lllumina Mi Seq sequencing technology. [ Result] A total of 31 phyla,84 orders,206 orders,347 families,655 genera,1045 species,and 1660 OTUs were identified from endophytic bacteria in stems of four fruit-shaped tomato varieties. However, there was no significant differences ( P > 0. 05) on the diversity and richness indices of the endophytic bacteria between each variety. At phylum level, Chloroflexi, Actinobacteriota, Bacteroidota, Firmicutes, Proteobacteria and Actinobaciota were the dominant endophytic bacteria in stems of four different fruit shapes tomato varieties. The composition of endophytic bacteria at phylum level was quite similar among four different fruit shapes tomato varieties. However, the proponions of different endophytic bacteria at phylum level in stems of four different frnit shapes tomato varieties were difference. At genus level, Ralstonia, Pseudonocardia, Brevibacterium, Brachybactenum, Bacillus and Rlwdococcus were the common dominant endophytic bacteria in stems of four different fruit shapes tomato varieties. PICRUSt functional prediction results showed that six types of biological metabolic pathways in the primary functional layer of endophytic bacterial genes could be found in stems of tomatoes with different fruit shapes, and the metabolic pathway was the main functions of endophytic bacteria in stems of four different fruit shapes of tomatoes. Meanwhile, although the types of predicted gene in secondary functional layer were similar, but their numbers were different in stems of different fruit shapes of tomatoes. [Conclusion] Although some common dominant endophytic bacteria at genus level could be found in stems of four different Jiuit shaped tomato varieties, but their proponions also showed in different ratios. Meanwhile, some unique dominant endophytic bacteria at genus level also could be detected in four different fruit shaped tomato varieties, respectively. It suggested that the unique dominant endophytic bacteria in stems of tomatoes at genus level and their functions, could be strongly related to the fruit shapes forming processes. It also indicates that the composition of endophytic bacteria in stems of tomatoes can be used as a bio-indicator with great potential for evaluating fruit shapes. [ABSTRACT FROM AUTHOR]

Firmness is one of the important indicators for the internal quality of fruits. Natural frequency has widely been expected to evaluate the fruit firmness in recent years. Several indices have successfully been applied for the firmness of sphere-shaped fruits using the natural frequency and fruit mass. However, the fruit with non-spherical or irregular shapes cannot be suitable for firmness evaluation, due mainly to the fact that the fruit shape can interfere with the resonance frequency. Thus, some dual-frequency indices were established to measure the firmness of fruit with ellipsoidal shapes. Nevertheless, these indices cannot accurately evaluate the firmness of complex and irregular pears. In this study, a Fourier descriptor was established as a reliable shape descriptor to evaluate the firmness, thereby representing complex, irregular, or a larger variation of pear shapes. Firstly, the shape images of pears were captured by a camera. Then, the boundary edges of pear images were extracted to calculate the normalized radiuses. After that, a Fourier transform was performed on the normalized radius to obtain the Fourier descriptor. Finally, the first 15 Fourier descriptors were screened to describe the complex shapes of pears. Meanwhile, an experimental modal combined with the Finite Element Method (FEM) was utilized to analyze the effect of fruit shape on the natural frequency. Three modes included bending, compression, and breathing during a test. The natural frequency was easy to measure the equatorial region of the pear fruit in the compression mode. The largest deformation appeared, when the pear was impacted on this region. As such, the pear was assumed to be linearly elastic, homogeneous and isotropic material in the FEM. A process of reverse engineering was utilized to form the geometric model of pears. The 3D 4-node tetrahedral structural solid element was selected, allowing three translational and three rotational degrees of freedom at each node. Consequently, three shapes and natural frequencies in FE modal analysis were highly related to the experimental data. Therefore, the FEM model was expected to analyze the influence of Fourier descriptors on the natural frequency of the pear. These results indicated that the first Fourier descriptor (F1) had a significant effect on the natural frequencies of three modes. The frequency decreased linearly for the bending and compression mode, and increased linearly for the breathing mode, with the increasing of the F1. A new firmness index was established using the relationship between the F1 and frequency in the compression mode. Finally, an M-T penetration test was conducted to investigate the correlation between the nondestructive and destructive firmness indices. By comparison, the Coefficient of Determination (COD) was 0.892 for the new index SE(F1) with the destructive M-T firmness tester. Two dual-frequency firmness indices for the ellipsoidal fruit and firmness index SE(q), the COD were 0.733, 0.775, 0.765, and 0.746, respectively, for traditional spherical firmness index. Therefore, the new firmness index can accurately evaluate the firmness of complex and irregular shape pears. Consequently, this finding can provide promising practical guidance for the nondestructive detection of the firmness of differently shaped pears using resonance in intelligent agriculture. [ABSTRACT FROM AUTHOR]

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