Survey of wheat straw stem characteristics for enhanced resistance to lodging

Ali, Muhammad; Hao, Huanhuan; Xue, Yali; Alam, Aftab; Bai, Shuming; Hu, Weicheng; Sajid, Muhammad; Hu, Zhen; Samad, Rana Abdul; Li, Zihui; Liu, Peiyao; Gong, Zhiqiang; Wang, Lingqiang

doi:10.1007/s10570-020-02972-7

Cited by 42 publications

(36 citation statements)

References 55 publications

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“…Bread wheat (Triticum aestivum L.) is a staple food crop worldwide, providing 20% of the caloric intake of humans [1,2]. Food production needs to be increase by an estimated 50% in order to keep pace with the increasing demands projected for 2050.…”

Section: Introductionmentioning

confidence: 99%

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Ali

et al. 2020

IJMS

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Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.

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Section: Introductionmentioning

confidence: 99%

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Ali

et al. 2020

IJMS

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“…During the rice threshing process, straw is subjected to the force of threshing bar and concave screen. The mechanical of rice stalks and leaves determine the fracture characteristics [13][14][15] . If we need to reveal the relationship between the breaking characteristics of rice leaves and the tensile breaking force after threshing, the tensile breaking force of rice leaves under different maturity conditions needs to be further studied.…”

Section: Introduction mentioning

confidence: 99%

Differences in breaking behavior of rice leaves under microwave and naturally drying processes

Wang

Tang

Zhang

et al. 2022

International Journal of Agricultural and Biological Engineering

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In order to fight for good farming time or harvest in bad weather, combine harvester will produce more broken leaves when threshing high moisture content rice, which will seriously hinder the grain cleaning effect. In this study, the breaking behavior of rice leaves under different microwave drying time and drying power was studied. Firstly, based on the appearance of freshly rice leaves undergoing microwave drying, the changing property of moisture content, weightlessness rate and temperature rise of rice leaves were tested and analyzed. Secondly, the tensile breaking force of freshly rice leaves under different microwave drying time and drying power was tested and compared with the tensile breaking force of naturally dried rice. Finally, the optimal microwaves parameters of rice leaves after drying which could result in greater breaking force than the natural drying state were obtained. The result showed that microwave drying method will reduce the moisture content of rice leaves and change the microstructure, which would affect the tensile-breaking property of rice leaves. The maximum tensile breaking force of rice leaves appeared at microwave dried power 70 W for 5-8 min and at microwave dried power 210 W for 3-4 min. The tensile breaking property of rice leaves at microwave dried power 350 W for 6-8 min were the weakest, which was lower than that of fresh rice leaves. Therefore, the optimal microwave drying parameters of rice leaves will provide a basis for the application of microwave technology in the threshing process.

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“…Wheat is the main food source in the world, the quality and yield of which are related to food security [ 1 ]. Lodging is a common agricultural natural disaster in wheat production, especially in the middle and late stages of wheat growth, and it is one of the important factors that limit the high yield of wheat [ 2 ]. On the one hand, lodging changes the individual development of wheat and, on the other hand, lodging changes the population structure of wheat.…”

Section: Introductionmentioning

confidence: 99%

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model

Yang

Zhu

Zhou

2021

Sensors

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The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has low accuracy and poor real-time performance. To overcome this gap, first, four-channel fusion images, including RGB and DSM (digital surface model), as well as RGB and ExG (excess green), were constructed based on the RGB image acquired from unmanned aerial vehicle (UAV). Second, a Mobile U-Net model that combined a lightweight neural network with a depthwise separable convolution and U-Net model was proposed. Finally, three data sets (RGB, RGB + DSM and RGB + ExG) were used to train, verify, test and evaluate the proposed model. The results of the experiment showed that the overall accuracy of lodging recognition based on RGB + DSM reached 88.99%, which is 11.8% higher than that of original RGB and 6.2% higher than that of RGB + ExG. In addition, our proposed model was superior to typical deep learning frameworks in terms of model parameters, processing speed and segmentation accuracy. The optimized Mobile U-Net model reached 9.49 million parameters, which was 27.3% and 33.3% faster than the FCN and U-Net models, respectively. Furthermore, for RGB + DSM wheat lodging extraction, the overall accuracy of Mobile U-Net was improved by 24.3% and 15.3% compared with FCN and U-Net, respectively. Therefore, the Mobile U-Net model using RGB + DSM could extract wheat lodging with higher accuracy, fewer parameters and stronger robustness.

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Survey of wheat straw stem characteristics for enhanced resistance to lodging

Cited by 42 publications

References 55 publications

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Differences in breaking behavior of rice leaves under microwave and naturally drying processes

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model

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