Deep Learning-Based Small-Object Detection in UAV Imagery: A Symmetry-Informed Review of Scale Variation and Scenario Constraints
综述无人机图像小目标检测的深度学习研究，提出对称性视角与三层框架。
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农业人工智能 目标检测
同时包含 农业人工智能、目标检测 的农业资讯，共 14 条。
A tomato maturity detection method against occlusion and variable illumination
提出一种抗遮挡和光照变化的番茄成熟度检测方法。
Full shuffle and p-rectified semi-inner powerful IoU-based chili pepper flower recognition with environment-aware YOLO11
基于YOLO11改进，实现温室辣椒花精准检测。
YOLO-Based Deep Learning Pipeline for Detecting Nutrient Deficiency Symptoms in Roselle (Hibiscus sabdariffa L.)
用YOLO系列模型检测玫瑰茄钙钾铁磷缺乏的视觉症状。
BiFormer-Enhanced YOLOv11n for Accurate Maize Ear Detection in Seed Production Fields(玉米穗识别 BiF-YOLO)
提出BiF-YOLO模型，在制种田实现玉米穗精准检测。
Lightweight Detection of Blueberries at Different Maturity Stages in Complex Orchard Environments(YOLOv12n+SHSA2C2f+知识蒸馏)
提出轻量YOLOv12n改进模型，实现复杂果园中多成熟期蓝莓的实时检测。
Fruit-OIC: An anti-occlusion flower and fruit detection algorithm for dragon fruit using dynamic case mining and global feature enhancement
提出抗遮挡的火龙果花果检测算法Fruit-OIC，结合动态案例挖掘与全局特征增强。
Mushroom cap phenotype extraction based on YOLOv11-SMD
提出YOLOv11-SMD模型，用于蘑菇菌盖表型自动提取。
An improved RT-DETR approach for real-time detection of densely occluded blueberries and micro-calyxes
提出改进RT-DETR的MFE-DETR模型，实现密集遮挡下蓝莓及微小花萼的实时检测。
Real-time hass avocado ripeness detection using close-range UAV imagery and YOLO-based deep learning
用无人机近景图像和YOLO深度学习实时检测哈斯鳄梨成熟度，实现精准农业应用。
Oil Palm Detection from High Resolution Remote Sensing Imagery with Improved YOLO Deep Learning Frameworks
改进YOLO11模型，集成注意力机制提升油棕树遥感检测精度与泛化能力。
Instance Segmentation and Multi-Object Tracking for Fruit Quality Grading and Dynamic Yield Estimation in Egyptian Citrus Orchards
构建柑橘果园AI框架，实现果实质量分级与动态产量估计，并评估多种分割与跟踪模型。
Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques
系统综述目标检测三十年发展，从传统方法到深度学习、Transformer及大语言模型，并分析数据集与
A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments (复杂果园环境中芒果果实与果柄联合检测的特征增强框架)
MDPI Agriculture 2026, 16(17), 1888, 2026年8月31日发表,作者来自华南农业大学工程学院和农业装备技术国家重点实验室。研究提出MangoNET,基于YOLOv11n的芒果果实与果柄联合检测框架,适用于复杂果园环境。P2高分辨率检测头保留小目标的精细空间信息,SPPF-ELAN聚合局部和上下文特征用于部分可见对象,SENet在光照变化下重新校准通道响应,WIoU v3调节具有不同定位质量的边界框样本。数据集包含1782张台农和金煌芒果原始图像,仅对训练集应用数据增强,将训练集大小从1172张增加到2886张图像。MangoNET果实和果柄F1分数分别为0.920和0.916,mAP50和mAP50-95值为0.941和0.690。