ultralytics-main.zip

Addressing the complexity and diversity of field symptoms related to citrus Huanglong disease (HLD), which significantly impacts yield and quality, while also tackling the challenges associated with the low efficiency and inadequate accuracy of traditional detection methods, this study introduces a field symptom detection model based on an enhanced version of YOLOv8. Initially, image enhancement techniques and other preprocessing steps were applied to samples of citrus Huanglong disease collected from various locations in Fujian Province to improve the model's capability to detect HLB in complex environments. Subsequently, a novel C2f Attention IRMB module was developed to replace the original cross-stage local layer convolution C2f, thereby providing a more comprehensive consideration of target position and size differences, improving target localization, and enhancing the model's ability to extract relevant features of citrus Huanglong disease while achieving model lightweighting. Additionally, a three-channel aggregated attention module was integrated into the Neck Powerneck, effectively capturing richer global and local image information, fostering interaction and fusion among different feature layers, and reducing redundancy and repeated computations to enhance the overall efficiency and performance of the model. The model's capacity to express features of citrus Huanglong disease was further strengthened by embedding a finely designed attention mechanism. Lastly, an efficient detection head was constructed to accelerate the inference process. Experimental results demonstrate that on a dataset comprising 12 diseases and 2 healthy states, the mean Average Precision at IoU 0.50 (mAP50) of this model achieved 97%, with a Precision of 91.5%; these figures reflect improvements of 1.1% and 1.2%, respectively, compared to the original YOLOv8 algorithm. Additionally, the inference speed increased by 14.6%, fully meeting the real-time requirements for detecting diseases in citrus fields and illustrating the effectiveness and advanced nature of the improved algorithm, thereby providing robust support for the rapid identification of diseases in the citrus cultivation process.

针对柑橘黄龙病（HLD）田间症状的复杂性与多样性——该病对产量和品质影响显著——同时解决传统检测方法效率低下、准确性不足的问题，本研究提出一种基于改进版YOLOv8的田间症状检测模型。首先，对从福建省多地采集的柑橘黄龙病样本进行图像增强技术及其他预处理步骤，以提升模型在复杂环境下检测HLB的能力。随后，开发新型C2f Attention IRMB模块替代原有的跨阶段局部层卷积C2f，从而更全面地考虑目标位置与大小差异，改善目标定位效果，增强模型提取柑橘黄龙病相关特征的能力，同时实现模型轻量化。此外，将三通道聚合注意力模块集成至Neck Powerneck中，有效捕捉更丰富的全局与局部图像信息，促进不同特征层间的交互与融合，减少冗余和重复计算，提升模型整体效率与性能。通过嵌入精心设计的注意力机制，进一步强化模型对柑橘黄龙病特征的表达能力。最后，构建高效检测头以加速推理过程。实验结果表明，在包含12种病害和2种健康状态的数据集上，该模型交并比（IoU）为0.50时的平均精度均值（mAP50）达97%，精确率（Precision）为91.5%；与原始YOLOv8算法相比，分别提升1.1%和1.2%。此外，推理速度提升14.6%，完全满足柑橘田间病害检测的实时性要求，验证了改进算法的有效性与先进性，为柑橘种植过程中的病害快速识别提供了有力支撑。

搜集汇总

背景与挑战

背景概述

该数据集包含用于柑橘黄龙病检测的预处理图像样本和改进的YOLOv8模型相关数据，旨在提高复杂环境下的病害检测效率和准确性。实验结果显示，改进后的模型在mAP50和精确度上均有显著提升，并实现了更快的推理速度。