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| 1 | Determination of rice panicle numbers during heading by multi-angle imaging显示文摘Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping. | Lingfeng Duan Chenglong Huang Guoxing Chen Lizhong Xiong Qian Liu Wanneng Yang | 2015 | The Crop Journal2015，3，3： | 18 |
| 2 | A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits显示文摘Lodging is a common problemin rice,reducing its yield andmechanical harvesting efficiency.Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity.The ideal rice culm structure,includingmajor_axis_culm,minor axis_culm,andwall thickness_culm,is critical for improving lodging resistance.However,the traditionalmethod ofmeasuring rice culms is destructive,time consuming,and labor intensive.In this study,we used a high-throughput micro-CT-RGB imaging system and deep learning(SegNet)todevelopa high-throughputmicro-CTimageanalysis pipelinethatcanextract 24 riceculmmorphological traits and lodging resistance-related traits.When manual and automatic measurements were compared at themature stage,the mean absolute percentage errors for major_axis_culm,minor_axis_culm,andwall_thickness_culmin 104 indica rice accessionswere 6.03%,5.60%,and 9.85%,respectively,and the R^(2) valueswere 0.799,0.818,and 0.623.We also builtmodels of bending stress using culmtraits at the mature and tillering stages,and the R^(2) values were 0.722 and 0.544,respectively.The modeling results indicated that this method can quantify lodging resistance nondestructively,even at an early growth stage.In addition,we also evaluated the relationships of bending stress toshoot dryweight,culm density,and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass,culm density,and culm area but poorer drought resistance.In conclusion,we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in4.6 min per plant;this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance. | Di Wu Dan Wu Hui Feng Lingfeng Duan Guoxing Dai Xiao Liu Kang Wang Peng Yang Guoxing Chen Alan P.Gay John H.Doonan Zhiyou Niu Lizhong Xiong Wanneng Yang | 2021 | Plant Communications2021，2，2： | 7 |
| 3 | Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging显示文摘 | Duan Lingfeng Yang Wanneng Bi Kun | 2011 | Computers and Electronics in Agriculture2011，75，1： | 1 |
| 4 | Rice panicle length measuring system based on dual-camera imaging显示文摘 | Huang Chenglong Yang Wanneng Duan Lingfeng | 2013 | Computers and Electronics in Agriculture2013，98，： | 1 |
| 5 | Fast discrimination and counting of filled/unfilled rice spikelets based on bi- modal imaging 显示文摘 | Duan Lingfeng Yang Wanneng Bi Kun | 2011 | Computers and Electronics in Agriculture2011，75，1： | 1 |
| 6 | A faster immunofluorescence assay for tracking infection progress of human cytomegalovirus显示文摘Immunofluorescence 试金(IFA ) 是在生物科学和诊所诊断的最经常使用的方法之一,但是它昂贵、费时间。克服这些限制,我们由修改标准 IFA 开发了更快、更划算的 IFA (f-IFA ) ,并且使用了这个方法追踪人的 cytomegalovirus (HCMV ) 的前进在不同房间的感染。我们开发了的 f-IFA 不仅节省时间,而且戏剧性地减少抗体(Ab ) 的数量,它将在诊所诊断便于 IFA 的应用程序。f-IFA 为堵住,为主要的各个的 10 min 孵化和第二等的 Abs 要求仅仅 15 min,由 1 min 列在后面广泛在每孵化以后洗。仅仅冲淡的 Ab 答案的 25 l 在主要、第二等的 Ab 孵化步骤为每 coverslip 被需要。另外,所有步骤在房间温度被执行。这 f-IFA 成功地被使用了跟随 virion 入口(pp65 ) 和病毒的基因(IE1, UL44,和 pp65 ) 的表示以便追踪 HCMV 感染过程的细节。我们发现 0.5%感染 HCMV 的 T98G 房间形成了 multiple-micronuclei (IE1 并且原子核染色) 并且有由 f-IFA 的流的病毒(染色的 pp65 ) ,它不能被传统的 IFA 检测。我们的结果显示 f-IFA 是为调查病毒感染进步的细节的一个敏感、方便、快、划算的方法,特别 HCMV 感染。有更高的敏感和特性的更快、划算的特征暗示 f-IFA 在临床的诊断有潜在的应用。 | Yingliang Duan Lingfeng Miao Hanqing Ye Cuiqing Yangl Bishi Fu Philip H.Schwartz Simon Rayner Elizabeth A.Formnato Min-Hua Luo | 2012 | Acta Biochimica et Biophysica Sinica2012，44，7： | 1 |
| 7 | Acceleration of CT Reconstruction for Wheat Tiller Inspection Based on Adaptive Minimum Enclosing Rectangle显示文摘 | Jiang Ni Yang Wanneng Duan Lingfeng | 2012 | Computers and Electronics in Agriculture2012，85，5： | 1 |
| 8 | High-throughput volumetric reconstruction for 3D wheat plant architecture studies显示文摘For many tller crops,the plant archit ecture(PA),including the plant fresh weight,plant height,number of tllrs,tller angle and stem diameter,sigificantly afects the grain yield.In this study,we propose a method based on volumetric reconstruction for high-throughput three-dimensional(3D)wheat PA studies.The proposed methodology involves plant volumetric reconst ruction from multiple images,plant model processing and phenotypic parameter estimation and analysis.This study was performed on 80 Triticum aestium plants,and the results were analyzed.Comparing the automated measurements with manual measurements,the mean absolute per-centage error(MAPE)in the plant height and the plant fresh weight was 2.71%(1.08cm with an average plant height of 40.07cm)and 10.06%(1.41g with an average plant fresh weight of 14.06 g),respectively.The root mean square error(RMSE)was 137 cm and 1.79g for the plant height and plant fresh weight,respectively.The correlation cofficients were 0.95 and 0.96 for the plant height and plant fresh weight,respectively.Additionally,the proposed methodology,in-cluding plant reconstruction,model processing and trait ext raction,required only approximately 20s on average per plant using parallel computing on a graphics processing unit(GPU),dem-onstrating that the methodology would be valuable for a high-throughput phenotyping platform. | Wei Fang Hui Feng Wanneng Yang Lingfeng Duan Guoxing Chen Lizhong Xiong Qian Liu | 2016 | Journal of Innovative Optical Health Sciences2016，9，5： | 1 |
| 9 | An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning显示文摘Rice panicle phenotyping is required in rice breeding for high yield and grain quality.To fully evaluate spikelet and kernel traits without threshing and hulling,using X-ray and RGB scanning,we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline.We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy(R~2 of 0.99)and speed.Faster R-CNN was also applied to indica and japonica classification and achieved 91%accuracy.The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding. | Lejun Yu Jiawei Shi Chenglong Huang Lingfeng Duan Di Wu Debao Fu Changyin Wu Lizhong Xiong Wanneng Yang Qian Liu | 2021 | The Crop Journal2021，9，1： | 1 |
| 10 | A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images显示文摘Total green leaf area(GLA)is an important trait for agronomic studies.However,existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive.A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented.Using projected areas of the plant in images,linear,quadratic,exponential and power regression models for estimating total GLA were evaluated.Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area.And power models fit better than other models.In addition,the use of multiple side-view images was an efficient method for reducing the estimation error.The inclusion of the top-view projected area as a seoond predictor provided only a slight improvement of the total leaf area est imation.When the projected areas from multi angle images were used,the estimated leaf area(ELA)using the power model and the actual leaf area had a high correlation cofficient(R2>0.98),and the mean absolute percentage error(MAPE)was about 6%.The method was capable of estimating the total leaf area in a nondestructive,accurate and eficient manner,and it may be used for monitoring rice plant growth. | Ni Jiang Wanneng Yang Lingfeng Duan Guoxing Chen Wei Fang Lizhong Xiong Qian Liu | 2015 | Journal of Innovative Optical Health Sciences2015，8，2： | 1 |
| 11 | Panicle-3D: A low-cost 3D-modeling method for rice panicles based on deep learning, shape from silhouette, and supervoxel clustering显示文摘Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on 3 D panicle phenotyping has been limited. Given that existing 3 D modeling techniques do not focus on specified parts of a target object, an efficient method for panicle modeling of large numbers of rice plants is lacking. This paper presents an automatic and nondestructive method for 3 D panicle modeling. The proposed method integrates shoot rice reconstruction with shape from silhouette, 2 D panicle segmentation with a deep convolutional neural network, and 3 D panicle segmentation with ray tracing and supervoxel clustering. A multiview imaging system was built to acquire image sequences of rice canopies with an efficiency of approximately 4 min per rice plant. The execution time of panicle modeling per rice plant using 90 images was approximately 26 min. The outputs of the algorithm for a single rice plant are a shoot rice model, surface shoot rice model, panicle model, and surface panicle model, all represented by a list of spatial coordinates. The efficiency and performance were evaluated and compared with the classical structure-from-motion algorithm. The results demonstrated that the proposed method is well qualified to recover the 3 D shapes of rice panicles from multiview images and is readily adaptable to rice plants of diverse accessions and growth stages. The proposed algorithm is superior to the structure-from-motion method in terms of texture preservation and computational efficiency. The sample images and implementation of the algorithm are available online. This automatic, cost-efficient, and nondestructive method of 3 D panicle modeling may be applied to high-throughput 3 D phenotyping of large rice populations. | Dan Wu Lejun Yu Junli Ye Ruifang Zhai Lingfeng Duan Lingbo Liu Nai Wu Zedong Geng Jingbo Fu Chenglong Huang Shangbin Chen Qian Liu Wanneng Yang | 2022 | The Crop Journal2022，10，5： | 1 |
| 12 | Experimental and Numerical Analysis of the Relationship between Pressure and Drip Rate in a Vertical Polypropylene Infusion Bag显示文摘Vertical infusion(self-emptying)bags used for Intravenous infusion are typically obtained by moulding a soft envelope of polypropylene.In normal conditions a continuous flow of liquid can be obtained with no need to use a pump.In the present study,the relationship between air pressure effects and the drug drip rate have been investigated experimentally and numerically.After determining relevant experimental data about the descending height of liquid level,the dropping speed and pressure,the ordinary least square method and MATLAB have been used to reconstruct the related variation and interrelation laws.Numerical simulations have been performed to determine the best gas-liquid volume ratio and improve the overall performances of these bags.According to these results,that the biggest effect on the drip rate is produced by the diameters of the used needles. | Weiwei Duan Lingfeng Tang | 2021 | Fluid Dynamics & Materials Processing2021，17，6： | 0 |
| 13 | Experimental study on the protective effect of ulinastatin on lung tissue in rats with severe scalded显示文摘Objective:To explore the potential protective effects of ulinastatin on ventilation-induced lung injuries of severe burned rats.Methods:Ninety Wistar rats were randomly divided into three experimental groups:the control group(n=30),the ventilation group(n=30)and the ventilation-ulinastatin group(n=30).After establishing the severe burn model,the rats of latter two groups were mechanically ventilated for 1 hour with or without the pre-treatment of ulinastatin.After severe scald,the protective effect of ulinastatin on lung injury caused by mechanical ventilation was estimated through the observation of the tissues samples,and evaluation of the pathological changes of lung tissue by HE staining,ultrastructure change by electron microscopy,lung coefficient,and the expression levels of lung tissue cytokines TNF-α,IFN-γ,IL-2 by immunohistochemical staining.Results:Edema in lung tissues of the control group and the ventilation group was obvious,the hemorrhagic focus could be seen,and the cut surface was observed to be scattered and swelling;Edema in lung tissues of the ventilation-ulinastatin group was mild.HE staining revealed that the pathological changes of the ventilation-ulinastatin group were milder than the ventilation group.Under the electron microscope,the lung tissue organelles of the control group and the ventilation group were seriously damaged;the corresponding changes in the ventilation-ulinastatin group were lighter.The lung coefficient of the ventilation-ulinastatin group was significantly lower than that in the ventilation group.The immunohistochemical results showed that the intensity of TNF-α,IL-2 and IFN-γin lung tissue of the ventilation-ulinastatin group was significantly lower than that in the ventilation group.Conclusions:Ulinastatin has protective effects on lung injury caused by mechanical ventilation in severe scalded rats,whose mechanism may be related to the capacity of ulinastatin to reduce the expression of cytokines including TNF-α,IL-2 and IFN-γ. | Xiaguang Duan Lingfeng Wang | 2016 | Discussion of Clinical Cases2016，3，1： | 0 |
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