Subjects: Statistics >> Social Statistics submitted time 2023-12-04 Cooperative journals: 《智慧农业（中英文）》
Abstract： Objective Wheat serves as the primary source of dietary carbohydrates for the human population, supplying 20% of the required caloric intake. Currently, the primary objective of wheat breeding is to develop wheat varieties that exhibit both high quality and high yield, ensuring an overall increase in wheat production. Additionally, the consideration of phenotype parameters, such as grain length and width, holds significant importance in the introduction, screening, and evaluation of germplasm resources. Notably, a noteworthy positive association has been observed between grain size, grain shape, and grain weight. Simultaneously, within the scope of wheat breeding, the occurrence of inadequate harvest and storage practices can readily result in damage to wheat grains, consequently leading to a direct reduction in both emergence rate and yield. In essence, the integrity of wheat grains directly influences the wheat breeding process. Nevertheless, distinguishing between intact and damaged grains remains challenging due to the minimal disparities in cer‐tain characteristics, thereby impeding the accurate identification of damaged wheat grains through manual means. Consequently, this study aims to address this issue by focusing on the detection of wheat kernel integrity and completing the attainment of grain phenotype parameters. Methods This study presented an enhanced approach for addressing the challenges of low detection accuracy, unclear segmentation of wheat grain contour, and missing detection. The proposed strategy involves utilizing the Cascade Mask R-CNN model and replacing the backbone network with ResNeXt to mitigate gradient dispersion and minimize the model's parameter count. Furthermore, the inclusion of Mish as an activation function enhanced the efficiency and versatility of the detection model. Additionally, a multilayer convolutional structure was introduced in the detector to thoroughly investigate the latent features of wheat grains. The Soft-NMS algorithm was employed to identify the candidate frame and achieve accurate segmentation of the wheat kernel adhesion region. Additionally, the ImCascade R-CNN model was developed. Simultaneously, to address the issue of low accuracy in obtaining grain contour parameters due to disordered grain arrangement, a grain contour-based algorithm for parameter acquisition was devised. Wheat grain could be approximated as an oval shape, and the grain edge contour could be obtained according to the mask, the distance between the farthest points could be iteratively obtained as the grain length, and the grain width could be obtained according to the area. Ultimately, a method for wheat kernel phenotype identification was put forth. The ImCascade R-CNN model was utilized to analyze wheat kernel images, extracting essential features and determining the integrity of the kernels through classification and boundary box regression branches. The mask generation branch was employed to generate a mask map for individual wheat grains, enabling segmentation of the grain contours. Subsequently, the number of grains in the image was determined, and the length and width parameters of the entire wheat grain were computed. Results and Discussions In the experiment on wheat kernel phenotype recognition, a comparison and improvement were conducted on the identification results of the Cascade Mask R-CNN model and the ImCascade R-CNN model across various modules. Additionally, the efficacy of the model modification scheme was verified. The comparison of results between the Cascade Mask R-CNN model and the ImCascade R-CNN model served to validate the proposed model's ability to significantly decrease the missed detection rate. The effectiveness and advantages of the ImCascade R-CNN model were verified by comparing its loss value, P-R value, and mAP_50 value with those of the Cascade Mask R-CNN model. In the context of wheat grain identification and segmentation, the detection results of the ImCascade R-CNN model were compared to those of the Cascade Mask R-CNN and Deeplabv3+ models. The comparison confirmed that the ImCascade R-CNN model exhibited superior performance in identifying and locating wheat grains, accurately segmenting wheat grain contours, and achieving an average accuracy of 90.2% in detecting wheat grain integrity. These findings serve as a foundation for obtaining kernel contour parameters. The grain length and grain width exhibited average error rates of 2.15% and 3.74%, respectively, while the standard error of the aspect ratio was 0.15. The statistical analysis and fitting of the grain length and width, as obtained through the proposed wheat grain shape identification method, yielded determination coefficients of 0.9351 and 0.8217, respectively. These coefficients demonstrated a strong agreement with the manually measured values, indicating that the method is capable of meeting the demands of wheat seed testing and providing precise data support for wheat breeding. Conclusions The findings of this study can be utilized for the rapid and precise detection of wheat grain integrity and the acquisition of comprehensive grain contour data. In contrast to current wheat kernel recognition technology, this research capitalizes on enhanced grain contour segmentation to furnish data support for the acquisition of wheat kernel contour parameters. Additionally, the refined contour parameter acquisition algorithm effectively mitigates the impact of disordered wheat kernel arrangement, resulting in more accurate parameter data compared to existing kernel appearance detectors available in the market, providing data support for wheat breeding and accelerating the cultivation of high-quality and high-yield wheat varieties.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Basic Disciplines of Agriculture submitted time 2023-08-14 Cooperative journals: 《智慧农业（中英文）》
Abstract： [Objective] Rapid recognition and automatic positioning of table grapes in the natural environment is the prerequisite for the automat‐ic picking of table grapes by the picking robot. [Methods] An rapid recognition and automatic picking points positioning method based on improved K-means clustering algorithm and contour analysis was proposed. First, euclidean distance was replaced by a weighted gray threshold as the judgment basis of Kmeans similarity. Then the images of table grapes were rasterized according to the K value, and the initial clustering center was obtained. Next, the average gray value of each cluster and the percentage of pixel points of each cluster in the total pixel points were calculated. And the weighted gray threshold was obtained by the average gray value and percentage of adjacent clusters. Then, the clustering was considered as have ended until the weighted gray threshold remained unchanged. Therefore, the cluster image of table grape was obtained. The improved clustering algorithm not only saved the clustering time, but also ensured that the K value could change adaptively. Moreover, the adaptive Otsu algorithm was used to extract grape cluster information, so that the initial binary image of the table grape was obtained. In order to reduce the interference of redundant noise on recognition accuracy, the morphological algorithms (open operation, close operation, images filling and the maximum connected domain) were used to remove noise, so the accurate binary image of table grapes was obtained. And then, the contours of table grapes were obtained by the Sobel operator. Furthermore, table grape clusters grew perpendicular to the ground due to gravity in the natural environment. Therefore, the extreme point and center of gravity point of the grape cluster were obtained based on contour analysis. In addition, the linear bundle where the extreme point and the center of gravity point located was taken as the carrier, and the similarity of pixel points on both sides of the linear bundle were taken as the judgment basis. The line corresponding to the lowest similarity value was taken as the grape stem, so the stem axis of the grape was located. Moreover, according to the agronomic picking requirements of table grapes, and combined with contour analysis, the region of interest (ROI) in picking points could be obtained. Among them, the intersection of the grapes stem and the contour was regarded as the middle point of the bottom edge of the ROI. And the 0.8 times distance between the left and right extreme points was regarded as the length of the ROI, the 0.25 times distance between the gravity point and the intersection of the grape stem and the contour was regarded as the height of the ROI. After that, the central point of the ROI was captured. Then, the nearest point between the center point of the ROI and the grape stem was determined, and this point on the grape stem was taken as the picking point of the table grapes. Finally, 917 grape images (including Summer Black, Moldova, and Youyong) taken by the rear camera of MI8 mobile phone at Jinniu Mountain Base of Shandong Fruit and Vegetable Research Institute were verified experimentally. [Results and Discussions] The results showed that the success rate was 90.51% when the error between the table grape picking points and the optimal points were less than 12 pixels, and the average positioning time was 0.87 s. The method realized the fast and accurate localization of table grape picking points. On top of that, according to the two cultivation modes (hedgerow planting and trellis planting) of table grapes, a simulation test platform based on the Dense mechanical arm and the single-chip computer was set up in the study. 50 simulation tests were carried out for the four conditions respectively, among which the success rate of localization for purple grape picking point of hedgerow planting was 86.00%, and the average localization time was 0.89 s; the success rate of localization for purple grape identification and localization of trellis planting was 92.00%, and the average localization time was 0.67 s; the success rate of localization for green grape picking point of hedgerow planting was 78.00%, and the average localization time was 0.72 s; and the success rate of localization for green grape identification and localization of trellis planting was 80.00%, and the average localization time was 0.71 s. [Conclusions] The experimental results showed that the method proposed in the study can meet the requirements of table grape picking, and can provide technical supports for the development of grape picking robot.
Subjects: Materials Science >> Materials Science (General) submitted time 2023-03-31 Cooperative journals: 《材料研究学报》
Abstract： High-quality and few-layered graphene was grown by chemical vapor deposition (CVD) on copper foils, which were pre-treated by etching with 25%HCl or 2 mol/L FeCl3 and then electrochemical polishing in order to improve their surface smoothness. The surface morphology of the copper foils and the deposited graphene were characterized by means of Raman spectroscopy, XRD and SEM etc. The results showed that copper foils with desired surface smoothness would be acquired through etching with 2 mol/L FeCl3 for 30 s and then electrochemical polishing for 60 s by applied voltage of 10 V; Films of layered graphene with less defects could be deposited on the pre-treated copper foils. The thickness of graphene films increased with the increasing time, however for a short deposition time the formed graphene films were discontinuous with poor quality. The monolayered high- quality graphene films could be prepared by depositing for 30 s, whilst the deposition time increased to 60 s a graphite film could form on the surface. In other word, it is necessary to control the deposition on time for growing the desired monolayered graphene films.
Subjects: Biology >> Zoology submitted time 2018-12-25 Cooperative journals: 《动物营养学报》
Abstract：本试验旨在研究育成后期（10～17周龄）饲粮代谢能和蛋白质水平对大午粉1号商品代蛋鸡生长性能、器官指数、小肠发育以及产蛋高峰期生产性能和蛋品质的影响；通过建立饲粮代谢能和蛋白质水平与所检测指标之间的回归模型，得到育成后期大午粉1号商品代蛋鸡饲粮中适宜的代谢能和蛋白质水平。本研究共包括2个试验。试验1：随机选取810只64日龄蛋鸡，将其随机分为9组，每组6个重复，每个重复15只。采用3[代谢能水平：11.77（高）、11.27（中）、10.77 MJ/kg（低）]×3[蛋白质水平：16.50%（高）、15.50%（中）、14.50%（低）]试验设计，共配制9种试验饲粮，分别饲喂上述9组试验鸡，试验期8周（10～17周龄）。试验2：试验鸡的分组情况保持不变，所有试验鸡饲喂同一饲粮（代谢能水平：10.91 MJ/kg；蛋白质水平：15.98%），试验期14周（18～31周龄）。试验1结果显示：1）饲粮代谢能水平对蛋鸡平均日采食量（ADFI）、料重比（F/G）和胫骨长有显著影响（P＜0.05）；饲粮蛋白质水平对蛋鸡ADFI和胫骨长有显著影响（P＜0.05）；饲粮代谢能和蛋白质水平的互作效应对蛋鸡ADFI、平均日增重（ADG）和F/G均有显著影响（P＜0.05）。2）饲粮代谢能和蛋白质水平及二者的互作效应对蛋鸡各器官指数均无显著影响（P＞0.05）。3）饲粮代谢能水平对蛋鸡空肠、十二指肠、小肠长度有显著影响（P＜0.05）；饲粮代谢能和蛋白质水平的互作效应对蛋鸡空肠和小肠长度有显著影响（P＜0.05）。试验2结果显示：1）育成后期饲粮代谢能和蛋白质水平对蛋鸡产蛋高峰期生产性能均没有显著影响（P＞0.05），随着育成后期饲粮代谢能水平的升高，蛋鸡产蛋高峰期ADFI呈下降趋势；育成后期饲粮代谢能和蛋白质水平的互作效应对蛋鸡产蛋高峰期ADFI、平均日产蛋量、料蛋比（F/E）有显著影响（P＜0.05）。2）育成后期蛋鸡饲粮代谢能水平对蛋鸡产蛋高峰期蛋黄颜色和蛋形指数有显著影响（P＜0.05）；育成后期蛋鸡饲粮蛋白质水平对蛋鸡产蛋高峰期蛋黄颜色有显著影响（P＜0.05）；育成后期饲粮代谢能和蛋白质水平的互作效应对蛋鸡产蛋高峰期蛋壳厚度和蛋形指数有显著影响（P＜0.05）。通过对育成后期ADFI、F/G以及空肠、十二指肠、小肠长度和蛋黄颜色进行二次曲线拟合，得出大午粉1号商品代蛋鸡育成后期饲粮中适宜代谢能水平分别为10.902、10.720、11.404、11.446、11.374和11.760 MJ/kg；通过对育成后期胫骨长进行二次曲线拟合，得出大午粉1号商品代蛋鸡育成后期饲粮中适宜蛋白质水平为15.300%。综合上述指标，推荐育成后期（10～17周龄）大午粉1号商品代蛋鸡饲粮中代谢能水平为10.720～11.760 MJ/kg，蛋白质水平为15.300%。
Subjects: Biology >> Zoology submitted time 2018-12-24 Cooperative journals: 《动物营养学报》
Abstract：本试验通过建立饲粮能量或蛋白质水平与生长性能、血清生化指标、器官指数等指标的回归模型，旨在确定5～9周龄大午粉1号商品代蛋雏鸡对饲粮能量和蛋白质的需要量。选取810只遗传背景相同、体重接近、健康状态良好的28日龄大午粉1号商品代蛋雏鸡，随机分为9组，每组6个重复，每个重复15只蛋雏鸡。采用3×3双因素试验设计，设定饲粮中能量水平分别为12.42、11.92和11.42 MJ/kg，蛋白质水平分别为18.75%、17.75%和16.75%，共配制9种试验饲粮。试验期为35 d。结果显示：1）随着饲粮能量水平的升高，蛋雏鸡9周龄时胫长、胸宽、龙骨长、血清甘油三酯含量呈上升的趋势，5～9周龄平均日增重呈先下降后上升的趋势。2）随着饲粮蛋白质水平的升高，蛋雏鸡9周龄时体重（终末体重）、胸宽以及5～9周龄平均日增重呈先升高后降低的趋势。3）饲粮能量水平与蛋白质水平的互作效应对9周龄大午粉1号商品代蛋雏鸡的胸宽、龙骨长、血清甘油三酯含量有显著影响（P＜0.05)。4）通过对蛋雏鸡胸宽、龙骨长、血清甘油三酯含量与饲粮能量水平进行二次曲线拟合，得出饲粮适宜能量水平分别为11.420、11.483、11.379 MJ/kg，平均值为11.427 MJ/kg；通过对9周龄时体重与饲粮蛋白质水平二次曲线拟合，得到饲粮适宜蛋白质水平为17.902%。综合蛋雏鸡体尺指标、生长性能、器官指数和血清生化指标得出，5～9周龄大午粉1号商品代蛋雏鸡的能量和蛋白质需要量分别为11.427 MJ/kg、17.902%。
Subjects: Biology >> Zoology submitted time 2018-12-24 Cooperative journals: 《动物营养学报》
Abstract：本试验旨在研究高、低钙饲粮交替饲喂对蛋鸡产蛋后期生产性能、蛋品质及血清指标的影响。试验选取68周龄的大午粉1号蛋种鸡64只，随机分为2组，每组32只，单笼饲养于个体智能鸡笼内，对照组08：00和14:00分别饲喂43.3、86.7 g中钙饲粮（钙含量为3.66%），试验组08：00饲喂43.3 g低钙饲粮（钙含量为2.00%），14:00饲喂86.7 g高钙饲粮（钙含量为4.49%）。试验期为5周，其中预试期1周，正试期为4周。结果显示：试验组各周的平均日采食量、平均蛋重、产蛋率、料蛋比与对照组无显著差异（P>0.05），但试验组各周的产蛋率在数值上高于对照组。试验组第2周的蛋壳厚度显著高于对照组（P<0.05），而试验组各周蛋白高度、哈氏单位与对照组无显著差异（P＞0.05）。对照组中，18:00和22:00的血清钙含量显著高于10:00和14:00（P<0.05），高于06:00和次日02:00（P＞0.05）；试验组中，22:00的血清钙含量极显著高于06:00、10:00、18:00和次日02:00（P<0.01），并显著高于14:00和18:00（P<0.05）；从06:00到22:00，试验组血清钙含量一直呈现增长的趋势，在22:00时，试验组血清钙、降钙素含量均显著高于对照组（P<0.05）。由此得出，高、低钙饲粮交替饲喂(将高钙饲粮在下午饲喂)可提高蛋鸡产蛋后期的产蛋率和对钙的吸收和沉积，并且可在一定程度上改善蛋壳质量。
Subjects: Biology >> Zoology submitted time 2018-12-24 Cooperative journals: 《动物营养学报》
Abstract：本试验旨在研究高、低钙饲粮交替饲喂对蛋鸡产蛋后期生产性能、蛋品质及血清指标的影响。试验选取68周龄的大午粉1号蛋种鸡64只，随机分为2组，每组32只，单笼饲养于个体智能鸡笼内，对照组08：00和14:00分别饲喂43.3、86.7 g中钙饲粮（钙含量为3.66%），试验组08：00饲喂43.3 g低钙饲粮（钙含量为2.00%），14:00饲喂86.7 g高钙饲粮（钙含量为4.49%）。试验期为5周，其中预试期1周，正试期为4周。结果显示：试验组各周的平均日采食量、平均蛋重、产蛋率、料蛋比与对照组无显著差异（P>0.05），但试验组各周的产蛋率在数值上高于对照组。试验组第2周的蛋壳厚度显著高于对照组（P<0.05），而试验组各周蛋白高度、哈氏单位与对照组无显著差异（P＞0.05）。对照组中，18:00和22:00的血清钙含量显著高于10:00和14:00（P<0.05），高于06:00和次日02:00（P＞0.05）；试验组中，22:00的血清钙含量极显著高于06:00、10:00、18:00和次日02:00（P<0.01），并显著高于14:00和18:00（P<0.05）；从06:00到22:00，试验组血清钙含量一直呈现增长的趋势，在22:00时，试验组血清钙、降钙素含量均显著高于对照组（P<0.05）。由此得出，高、低钙饲粮交替饲喂(将高钙饲粮在下午饲喂)可提高蛋鸡产蛋后期的产蛋率和对钙的吸收和沉积，并且可在一定程度上改善蛋壳质量。
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