Determination of rice panicle numbers during heading by multi-angle imaging

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.

An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning

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.

Panicle-3D: A low-cost 3D-modeling method for rice panicles based on deep learning, shape from silhouette, and supervoxel clustering

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.

Hot-melt sub-and outercoating combined with enteric aqueous coating to improve the stability of aspirin tablets

Aspirin is apt to hydrolyze. In order to improve its stability, a new method has been developed involving the application of hot-melt sub-and outercoating combined with enteric aqueous coating. The main aim was to investigate the influence of these factors on the stability of ASA and understand how they work. Satisfactory storage stability were obtained when the aspirin tablet core coated with Eudragit L30D55 film was combined with glycerin monostearate(GMS) as an outercoat. Hygroscopicity testing indicated that the moisture penetrating into the tablet may result in a significant change in the physical properties of the coating film observed by scanning electron microscopy. Investigation of the compatibility between the drug and film excipients shows that the talc and methacrylic acid had a significant catalytic effect on ASA. A hypothesis was proposed that the hydrolysis of ASA enteric coated tablets(ASA-ECT) was mostly concentrated in the internal film and the interfaces between the film and tablet core. In conclusion, hot-melt coating technology is an alternative to subcoating or outercoating. Also, GMS sub-coating was a better choice for forming a stable barrier between the tablet core and the polymer coating layer, and increases the structure and chemical stability.

Genome-Wide Association Studies of Image Traits Reveal Genetic Architecture of Drought Resistance in Rice

Understanding how plants respond to drought can benefit drought resistance （DR） breeding. Using a non-destructive phenotyping facility, 51 image-based traits （i-traits） for 507 rice accessions were extracted. These i-traits can be used to monitor drought responses and evaluate DR. High heritability and large variation of these traits was observed under drought stress in the natural population. A genome-wide as- sociation study （GWAS） of i-traits and traditional DR traits identified 470 association loci, some containing known DR-related genes. Of these 470 loci, 443 loci （94%） were identified using i-traits, 437 loci （93%） co- localized with previously reported DR-related quantitative trait loci, and 313 loci （66.6%） were reproducibly identified by GWAS in different years. Association networks, established based on GWAS results, revealed hub i-traits and hub loci. This demonstrates the feasibility and necessity of dissecting the complex DR trait into heritable and simple i-traits. As proof of principle, we illustrated the power of this integrated approach to identify previously unreported DR-related genes. OsPP15 was associated with a hub i-trait, and its role in DR was confirmed by genetic transformation experiments. Furthermore, i-traits can be used for DR linkage analyses, and 69 i-trait locus associations were identified by both GWAS and linkage analysis of a recom- binant inbred line population. Finally, we confirmed the relevance of i-traits to DR in the field. Our study pro- vides a promising novel approach for the genetic dissection and discovery of causal genes for DR.

Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography

The traits of rice panicles play important roles in yield assessment,variety classification,rice breeding,and cultivation management.Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive;moreover,these methods cannot obtain 3D grain traits.In this work,based on X-ray computed tomography,we proposed an image analysis method to extract twenty-two 3D grain traits.After 104 samples were tested,the R^(2) values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960,respectively.We also found a high correlation between the total grain volume and weight.In addition,the extracted 3D grain traits were used to classify the rice varieties,and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers.In conclusion,we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.

Comparative proteomic analysis of plasma from bipolar depression and depressive disorder:identification of proteins associated with immune regulatory

Bipolar disorder(BD)is a debilitating psychiatric mood dis-order affecting approximately 1%-3%of the population worldwide(Merikangas,2007).Bipolar disorder is characterized by recurrent episodes of depression,hypomania,mania,or mixed states,and it has a poor outcome,with high rates of relapse,lingering residual symptoms,cognitive impairment,and functional impairment(Moreno et al.,2007)Although various etiopathological hypotheses concerning the disease have been reported,the pathophysiology underlying BD remains poody understood(Gawryluk and Young,2011).

A Novel Intelligent System for Dynamic Observation of Cotton Verticillium Wilt

Verticillium wilt is one of the most critical cotton diseases,which is widely distributed in cotton-producing countries.However,the conventional method of verticillium wilt investigation is still manual,which has the disadvantages of subjectivity and low efficiency.In this research,an intelligent vision-based system was proposed to dynamically observe cotton verticillium wilt with high accuracy and high throughput.Firstly,a 3-coordinate motion platform was designed with the movement range 6,100 mm×950 mm×500 mm,and a specific control unit was adopted to achieve accurate movement and automatic imaging.Secondly,the verticillium wilt recognition was established based on 6 deep learning models,in which the VarifocalNet(VFNet)model had the best performance with a mean average precision(mAP)of 0.932.Meanwhile,deformable convolution,deformable region of interest pooling,and soft non-maximum suppression optimization methods were adopted to improve VFNet,and the mAP of the VFNet-Improved model improved by 1.8%.The precision–recall curves showed that VFNet-Improved was superior to VFNet for each category and had a better improvement effect on the ill leaf category than fine leaf.The regression results showed that the system measurement based on VFNet-Improved achieved high consistency with manual measurements.Finally,the user software was designed based on VFNet-Improved,and the dynamic observation results proved that this system was able to accurately investigate cotton verticillium wilt and quantify the prevalence rate of different resistant varieties.In conclusion,this study has demonstrated a novel intelligent system for the dynamic observation of cotton verticillium wilt on the seedbed,which provides a feasible and effective tool for cotton breeding and disease resistance research.