Tuber indicum is an ectomycorrhizal ascomycete that produces edible ascocarps. Based on a number of specimens with known exact origin, we investigate the speciation of the Tuber indicum complex in southwest China. Internal transcribed spacer (ITS) and simple sequence repeat (SSR) markers were used in the study. Phylogeography and population genetics analyses were combined to detect 31 wild populations of the T. indicum complex. Two distinct lineages, Tuber cf. indicum and Tuber cf. himalayense, were identified in the T. indicum complex that exhibited significant phylogeographic structures and genetic differentiation. The characteristics of haplotypes distributing along the river demonstrate that the diffusion and modern distribution pattern of species was influenced by river expansion. These findings are critical for the protection of the diversity of truffles in this region.

Precise and real-time posture recognition of scallions during harvesting and post-harvest processing is critical for automated conveying, orientation adjustment, bundling, and packaging. Nevertheless, their slender and flexible form, varied spatial orientations, target overlap, lighting fluctuations, background interference, and constrained computational resources of edge devices present significant obstacles to reliable visual perception. This research introduces GOPD-YOLO, a lightweight oriented object-detection network built on the YOLOv8-OBB framework. The network integrates partial-convolution-based lightweight feature extraction to minimize redundant computation, large separable-kernel attention to boost long-range structural representation, and a shared detail-enhanced detection head to improve boundary- and orientation-sensitive prediction. A custom dataset comprising 1500 conveyor-belt images under diverse scallion posture scenarios was developed for model training and assessment. GOPD-YOLO attained a precision of 90.6%, a recall of 94.5%, an mAP@0.5 of 93.2%, and an mAP@0.5:0.95 of 71.5%, with 2.38 million parameters, 6.6 GFLOPs, and a model size of 4.9 MB. Relative to YOLOv8n-OBB, GOPD-YOLO enhanced recall by 3.8 percentage points while decreasing parameter count and model size by 22.7% and 22.2%, respectively. Deployment tests were performed on the Jetson Orin NX Super platform across varying conveyor speeds, lighting conditions, and scallion stacking levels to evaluate the model’s practical utility. These results indicate GOPD-YOLO’s potential as a lightweight vision-based solution for scallion posture recognition in automated harvesting and post-harvest processing.

Accurate prediction of greenhouse temperature and humidity variations was essential for optimizing crop growth conditions. To improve prediction accuracy under complex nonlinear environments, this study proposed an Improved Sparrow Search Algorithm–optimized Bidirectional Long Short-Term Memory model (ISSA-BILSTM). By introducing Tent chaotic mapping, an adaptive discoverer ratio mechanism, and a Lévy flight disturbance strategy, the proposed approach enhanced population diversity, balanced global exploration and local exploitation, and improved the convergence stability of hyperparameter optimization. Validation results showed that the proposed model achieved an R² of 0.9777 (MAE = 0.0159) for temperature prediction and an R² of 0.9762 (MAE = 0.0213) for humidity prediction, outperforming standard BILSTM and SSA-LSTM models. The proposed model enabled accurate short-term prediction of temperature and humidity, providing effective support for intelligent environmental regulation and contributing to reduced energy consumption and production costs.