Exploring the mysteries of plant disease severity through multimodal learning
Peer-Reviewed Publication
Updates every hour. Last Updated: 20-Oct-2026 18:16 ET (20-Oct-2026 22:16 GMT/UTC)
Researchers in China developed AgriMamba, a text-guided multimodal framework for plant disease severity grading in complex field environments. The framework combines disease images with pathological descriptions of lesion color, shape, edge characteristics, and spatial distribution. It follows a coarse-to-fine process that localizes the target leaf, segments lesion regions, and quantifies disease severity using lesion-to-leaf area ratios. Experimental and visualization results showed that AgriMamba achieved competitive performance compared with several representative deep learning methods. The model also progressively focused on target leaves and lesions despite background interference, leaf occlusion, and small or scattered disease regions. These findings provide a new technical approach for automated plant disease assessment in complex field environments.
Researchers in China identified that the TraesCS4B02G044100 (TaGeBPL-4B) is a candidate gene of the GNS using QTL mapping and GWAS. Knockout and overexpression studies showed that TaGeBPL-4B participates in the positive regulation of the GNS. The RNA-seq and DAP-seq analyses revealed that TraesCS5B02G120800 (TaAAP-5B) is a direct downstream target gene of TaGeBPL-4B. We further confirmed this regulation by Y1H, Dual-Luc and EMSA. TaGeBPL-4B activates the expression of TaAAP-5B and modulates amino acid contents, which possibly influence wheat spike development and GNS.
Researchers in China functionally characterized the branching habit regulatory gene AhPILS in peanut through MutMap analysis. This gene encodes a PIN-like protein that mediates polar auxin transport, thereby establishing an auxin gradient at the base of lateral branches. The formed auxin gradient potentially promotes cell elongation, which facilitates the morphological transition of peanut lateral branches from a spreading growth habit to an upright growth habit. This study identifies a novel functional gene, reveals key insights into the molecular regulatory mechanism of peanut branching habit, and provides elite germplasm resources for the molecular breeding of peanut cultivars with ideal plant architecture.
Researchers in China uncovered how cold priming leaves a lasting molecular memory in wheat, helping plants cope better when chilling returns. They found that the recovery phase is a critical window in which stress-responsive genes are reprogrammed and histone modifications are reshaped, while primed plants maintain better photosystem performance and show a reduced need for photosystem repair during later chilling. The findings reveal a key role for post-stress recovery in maintaining cold memory and provide new insight into how wheat adapts to repeated low-temperature stress.
A review published in Cyber-Physical Energy Systems examines how IoT and machine learning technologies can transform manufacturing energy management. By enabling real-time monitoring, prediction, and optimization, these approaches offer new pathways for energy-efficient and sustainable factories while addressing key challenges in industrial adoption.
Researchers have identified rainbow trout blood as a promising platform for monitoring the biological hazards of biodegradable microplastics. By combining advanced fluorescence imaging with single-cell analysis, the study reveals how these particles interact with circulating blood cells and disrupt immune-related processes. The findings also highlight an overlooked concern: environmental aging may alter and potentially intensify the harmful effects of plastics designed to biodegrade.