Waterlogging severely affects the growth, development, and yield of crops. Accurate high-throughput phenotyping is important for exploring the dynamic crop waterlogging response process in the field and the genetic basis of waterlogging tolerance. In this study, a multi-model remote sensing phenotyping platform based on an unmanned aerial vehicle (UAV) was used to assess the genetic response of rapeseed (Brassica napus) to waterlogging by measuring morphological traits and spectral indices in two years. The dynamic responses of the morphological and spectral traits indicated that the rapeseed waterlogging response was severe before the middle stage within 18 days after recovery, but it partly decreased subsequently. Genome-wide association studies identified 289 and 333 loci associated with waterlogging tolerance in two years. Next, 25 loci with at least nine associations with waterlogging-related traits were defined as highly reliable loci, and 13 loci were simultaneously identified by waterlogging tolerance coefficients of morphological traits, spectral indices, and common factors. Forty candidate genes were predicted in the regions of 13 overlapping loci. Our study provides insights into the understanding of dynamic process and genetic basis of rapeseed waterlogging response in the field by a high-throughput UAV phenotyping platform. The highly reliable loci identified in this study are valuable for breeding waterlogging-tolerant rapeseed cultivars.
The overarching goal of this study was to look into the effects of academic self-efficacy and academic motivation on student long-term engagement and academic achievement. This study also sought to investigate the role of learning agility as a mediator in the relationship between academic self-efficacy and academic motivation. This study examined the impact of student sustainable engagement on student academic achievement as part of our model. A questionnaire technique was used to collect data from 325 music education students studying at various music training institutions in China. The data were analyzed using the Smart-PLS software and a structural equation modeling (SEM) technique. Academic self-efficacy and academic motivation were found to have a positive and significant relationship with student long-term engagement. The academic motivation was also found to have a positive relationship with student long-term engagement. Furthermore, learning agility was found to mediate the relationship between academic self-efficacy and student sustainable engagement. Furthermore, learning agility mediated the relationship between academic motivation and long-term student engagement. Furthermore, student sustainable engagement has a significant and positive relationship with student academic achievement. This paper made a valuable theoretical contribution by investigating the impact of academic self-efficacy and academic motivation on student sustainable engagement, as well as the impact of student sustainable engagement on student academic achievement. Furthermore, this study added to the body of knowledge by investigating the relationship through the lens of cognitive learning theory. In terms of practical implications, this paper would undoubtedly assist educational institutions in maintaining a fair and just learning environment that encourages students to be engaged and perform well. Future research can include other constructs to gain a better understanding of the factors that influence students’ academic engagement and achievement.
The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, or simply called panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground measured PR reached 0.935, and the root mean square error (RMSE) values for the estimations of the heading date and effective tiller percentage were 0.687 days and 4.84%, respectively. Based on the result analysis, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAV and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops with future research.
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