Panicle-3D:Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud

The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth.The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement.At present,crop spike-shaped point cloud segmentation has problems such as fewer samples,uneven distribution of point clouds,occlusion of stem and spike,disorderly arrangement of point clouds,and lack of targeted network models.The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location,but the accuracy is not acceptable.This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process.Then,the rice ear point cloud was collected,and the rice ear point cloud data set was made.In addition,data argumentation is used to improve sample utilization efficiency and training accuracy.Finally,a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy.Specifically,the design of Panicle-3D is aimed at the multiscale characteristics of plant organs,combined with the structure of PointConv and long and short jumps,which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling.After comparison experiments,the segmentation accuracy of Panicle-3D reaches 93.4%,which is higher than PointNet.Panicle-3D is suitable for other similar crop point cloud segmentation tasks.

Bi-directional Selection in Upland Rice Leads to Its Adaptive Differentiation from Lowland Rice in Drought Resistance and Productivity

Drought resistance is required in rice breeding to address the challenge of frequent droughts . However, the evolutionary mechanism of rice drought resistance is not fully understood. We investigated the genetic differentiation between upland and lowland rice domesticated in agro-ecosystems with contrasting water-soil conditions using genome-wide SNPs. We estimated morphological differences among upland and lowland rice in drought resistance and productivity through common garden experiments. Upland rice had better drought resistance but poorer productivity. The negative correlations between traits of drought resistance and productivity are attributed to the underlying genetic trade-offs through tight linkages (e.g., DCA1 and OsCesA7) or pleiotropic effects (e.g., LAX1). The genetic trade-offs are comm on and greatly shape the evolutio n of drought resista nee in upland rice . In genomic regions associated with both productivity and drought resistance, signs of balancing selection were detected in upland rice, while signs of directional selection were detected in lowland rice, potentially contributing to their adaptive differentiation. Signs of balancing selection in upland rice resulted from bi-directional selection during its domestication in drought-prone upland agro-ecosystems.Using genome-wide association analysis, we ide ntified several valuable quantitative trait loci associated with drought resista nee, for which highly differentiated genes should be considered candidates. Bi-directional selection breaking tight linkages by accumulating recombination events would be applicable in breeding water-saving and droughtresistance rice.

Blue revolution for food security under carbon neutrality: A case from the water-saving and drought-resistance rice

The increasing concentration of greenhouse gases(GHGs)in Earth's atmosphere leads to global warming,which further causes a series of climate changes and does great harm to both human society and natural ecosystems.Agricultural GHG emissions,mainly in theform of methane(CH4)and nitrous oxide(N2O),areasignificantsourceofGHGs,accountingfor~14%total global GHGs(Zhang et al.,2022).One major source of agricultural GHGs is CH4 emissions from rice paddies,which is responsiblefor~10%-12%ofhuman-inducedCH4emissions(van Groenigen et al.,2013)and contributes~2.40%to the enhanced global warming effect(Zhang et al.,2022).The global warming potential of GHGs emissions from rice systems is roughly four times higher than either wheat or maize(Linquist et al.,2012).