面向种植农业复杂场景的YOLO算法应用综述

Survey of YOLO Algorithm Applications for Complex Scenarios in Crop Agriculture

摘要:

YOLO系列算法具有高推理速度、高检测精度和高度模块化的特点,已经成为智慧农业应用中目标检测的首选方法。本文系统梳理了YOLO算法在种植农业领域的研究进展与应用现状。首先,根据视觉任务的实现方式将种植农业领域视觉任务划分为果实检测类任务、农情分析类任务和采摘机器人视觉三大类,构建模型适配性评估框架,明确YOLO模型在各类任务中的适配程度及其应用现状。其次,针对复杂农业种植场景中普遍存在的小目标、遮挡、光照变化多样及算力限制等共性挑战,归纳主流的YOLO模型的改进策略,涵盖模型结构与模块优化、注意力机制引入等主流改进方法。最后,从视觉任务性能主导因素、高质量数据需求、模型版本选择和优化策略等方面综合分析YOLO系列算法在种植农业中的应用规律,并从感知能力突破与泛化能力提升两个方向展望未来发展趋势。

Abstract:

YOLO series algorithms, characterized by high inference speed, high detection accuracy, and a highly modular architecture, have become the preferred methods for object detection in smart agriculture. The research progress and application status of YOLO algorithms in the field of crop planting agriculture were systematically reviewsed. Firstly, according to the implementation mode of visual tasks, visual tasks in planting agriculture were classified into three categories: fruit detection tasks, crop condition analysis tasks, and robotic harvesting vision tasks. Based on this classification, a model suitability evaluation framework was constructed to clarify the adaptability and application status of YOLO models in different tasks. Secondly, in response to common challenges in complex agricultural planting scenarios, such as small targets, occlusion, diverse illumination conditions, and limited computing resources, the mainstream improvement strategies for YOLO models were summarized, including model structure and module optimization, as well as the introduction of attention mechanisms. Finally, from the perspectives of key factors dominating visual task performance, the demand for high-quality data, model version selection, and optimization strategies, the application patterns of YOLO series algorithms in planting agriculture were comprehensively analyzed, and the future development trends were discussed from two directions: breakthroughs in perception capability and improvements in generalization ability.