农学最新文献

Peanut (Arachis hypogaea) is widely cultivated worldwide as an important source of edible vegetable oil and protein. Peanut seed pods develop below ground from a gynophore that forms above ground and then penetrates the soil surface to bury the developing pod. Numerous studies have explored transcriptional regulation during peanut pod development. Here, we explored post-transcriptional regulation, including polyadenylation, alternative splicing, and RNA adenosine methylation (m⁶A), in peanut pods across four developmental stages by performing direct RNA sequencing. This produced 70.43 million long reads with average lengths of 890–1,136 nucleotides (nt) from 12 samples across four developmental stages, yielding a total of 14,627 newly identified transcripts. We detected a negative relationship between poly(A) tail lengths and transcript abundance, with the shortest poly(A) tails at the subterranean peg and expanded pod 1 stages, and longest poly(A) tails at the aerial gynophore and expanded pod 2 stages. Moreover, throughout pod development, from the penetration of the gynophore into the soil to pod enlargement, the splicing machinery utilized more proximal than distal alternative polyadenylation sites in the transcripts. The date showed no correlation between m⁶A modification and gene expression in peanut, but found more transcripts with alternative first and last exon types of alternative splicing events. Transcripts that were differentially abundant across developmental stages were primarily enriched in the Gene Ontology terms photosynthesis, response to oxidative stress, response to auxin, plant-type cell wall organization, and lignin catabolism. This study lays a foundation for revealing the roles of epigenetics and post-transcriptional regulation in pod development in peanut.

Fusarium Head Blight (FHB), a fungal wheat (Triticum aestivum) disease that threatens global food security, requires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding. Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting, which is inefficient and destructive. Although deep learning offers great promise for automated DSR measurement, existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data, insufficient feature representation for diseased spikelets, and weak spatial encoding of densely arranged spikelets. To address these challenges, we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field. We designed FHBDSR-Net, a light framework for automated DSR measurement centered on diseased spikelet detection, which features (1) multi-scale feature enhancement architecture that dynamically combines lesion textures, morphological features, and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise; (2) the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts; and (3) a scale-aware attention module using dilated convolutions and self-attention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution. FHBDSR-Net detected diseased spikelets with an average precision of 93.8% with a lightweight design of 7.2 M parameters. The results were strongly correlated with expert evaluations, with a Pearson correlation coefficient of 0.901. Our method is suitable for deployment on resource-constrained mobile devices, facilitating portable plant phenotyping and smart breeding.