Agriculture

Aux/IAA proteins function as central transcriptional repressors in auxin signaling and have been implicated in coordinating developmental responses to environmental stress, particularly through modulation of root system architecture. However, the contribution of auxin signaling components to drought-associated root plasticity in improving drought resilience in potato (Solanum tuberosum L.) remains unclear. In this study, we profiled Aux/IAA responses to water deficit across underground tissues by RNA sequencing of root tips, stolon tips, and tubers from two cultivars (Qingshu 9 and Atlantic) with contrasting drought tolerance. Drought treatment induced broad transcriptional changes in the Aux/IAA family, with the majority of members showing increased expression in at least one tissue. qRT-PCR across tissues and developmental stages validated distinct spatiotemporal patterns for selected candidates. Among these, the StIAA3, StIAA6, StIAA22, and StIAA25 genes displayed drought-inducible expression, whereas StIAA24 showed an opposite trend. To probe functional relevance, we generated overexpression and knockdown lines for StIAA3, StIAA6, StIAA22, and StIAA24. Altered expression of these genes was consistently associated with measurable changes in root architecture traits, including root length, diameter, and volume, under water-deficit conditions. These findings reveal insights into the contribution of auxin signaling components to drought-associated root plasticity in potato. The identified drought-responsive Aux/IAA candidates that link root architectural remodeling provide a foundation for mechanistic dissection and underground tissue remodeling of architecture enhancement in root crops.

The present study investigated the detection performance of the YOLOv8s, YOLO11s, and YOLO12s models, implemented within convolutional neural network architectures, for identifying floricane raspberry (Rubus idaeus L.) shrubs using RGB imagery and multispectral data acquired in the near-infrared, red-edge, red, and green spectral bands with a DJI Mavic 3 Multispectral drone. Model training and validation were conducted to evaluate both within-modality detection performance and cross-modality transferability. Under all training scenarios, the YOLO-based detectors reached near-saturated accuracy levels. However, cross-domain assessments demonstrated substantial variability depending on the spectral configuration of the input imagery. Overall, the combination of UAV-based multispectral sensing with convolutional neural network detection frameworks establishes a technological basis for automated shrub monitoring and constitutes a meaningful advancement toward intelligent raspberry production systems. This integration further creates new prospects for the technological development of cultivation practices for this crop within the rapidly evolving landscape of artificial intelligence-driven agriculture.

Sustainable indoor growing management requires biological alternatives that protect against pathogens, preserve fruit quality and minimise chemical inputs in strawberries. We compared the impacts of a four-strain Bacillus consortium (BacMix) and chemical fungicides on two cultivars (cv. Elsanta and cv. Sonsation) by evaluating the metabolite outcomes—the free amino acids (FAAs) in the leaves and the sugars in the fruits. Furthermore, the descriptive shotgun metagenomics provides a functional context for these biochemical traits. The BacMix increased the total FAAs in the leaves and stabilised the fruit sugar profiles, maintaining moderate–high sucrose with controlled glucose and fructose. The chemically treated plants showed significant reductions in both FAAs and sugars. The metagenomic data showed BacMix-related shifts in the microbial functional potential in the leaves, but the biological agent did not affect diversity. An increased representation of genes involved in amino acid biosynthesis (aminoacyl tRNA pathway) and secondary metabolite biosynthesis was observed, along with changes in the relative CAZy signals. The direction of these metagenomic trends aligned with the metabolite outcomes, suggesting that BacMix influences the endophytic microbiome in a way that supports nitrogen-related metabolism and carbohydrate stability during the vegetation period. The cultivar-independent metabolic improvements emphasise the benefits of BacMix and highlight microbiome-based interventions as promising tools for sustainable, chemical-reduced strawberry production.