#AI分选

提出改进YOLOv8n模型SDSI-YOLO，实现鲜茶叶粘连叶簇的高精度检测。

设计基于YOLO11n的辣椒种子逐粒精检分选机，实现高通量在线检测与无损剔除。

The growth status of tomato plug-tray seedlings varies continuously, with ambiguous boundaries between grades. Existing methods primarily focus on object detection and coarse-grained classification, making it difficult to fully exploit instance-level visual evidence for fine-grained seedling status assessment and interpretable sorting decisions. To address these limitations, this study proposes a detection-aware RGB-D vision–language framework. An RGB-D instance-aware detection model, RDIA-RTDETR, is developed to integrate RGB appearance and depth geometry through bidirectional cross-attention, enhancing the joint representation of leaf morphology, canopy structure, and plug-cell spatial characteristics to improve the detection accuracy of empty cells, weak seedlings, and Strong seedling. A Detection-aware Semantic Bridge (DSB) module is designed to transform object locations, class confidence scores, and detector query features into instance-level conditioning information, establishing a connection between object detection and subsequent semantic reasoning. Furthermore, a Detection-Aware Structured Seedling-status Report Generator (DA-SSRG) is proposed to integrate individual-seedling visual features with detection-bridge features through gated fusion. By incorporating hierarchical discrimination and risk-sensitive constraints, the model performs five-level seedling status grading and sorting decisions while generating corresponding diagnostic reasons and management recommendations. Experimental results show that RDIA-RTDETR achieves an F1-Score of 95.8% and an mAP50 of 97.3%, representing a 4.6-percentage-point improvement in F1-Score over RT-DETR-MM. The complete framework achieves accuracies of 92.73% and 94.17% for fine-grained grading and sorting decisions, respectively, with a Cause Score of 88.2%, a Suggestion Score of 91.5%, and an overall semantic consistency of 93.6%. The missed-identification rates for severely weak seedlings and samples requiring replacement are reduced to 2.3% and 1.0%, respectively. A web-based prototype enables structured report generation for individual plug cells and seedling status summarization at the tray level, providing technical support for abnormality screening, localization of cells requiring seedling replenishment, and fine-grained sorting decisions in plug-tray seedling production.

该文是一份关于2026年机器学习职业机会、技能与行业应用的报告框架。

综述人工智能在工业领域的应用，涵盖自动化、节能、安全与效率提升。

提出CNN框架对水果和作物叶片进行五级品质分级与缺陷识别。

探讨AI在环境监测与可持续发展中的作用，聚焦尼日利亚，涵盖遥感、水质、废物管理等应用。

提出YOLOv8-MST轻量算法，实现双粒花生播种实时检测与计数。