基于PG-YOLO v8s的黄瓜白粉病菌显微图像精准检测

Accurate Detection of Cucumber Powdery Mildew Fungus in Microscopic Images Based on PG-YOLO v8s

摘要:

黄瓜是世界上最重要的蔬菜和经济作物之一。黄瓜真菌病害的发生严重威胁着黄瓜的生产安全，白粉病是其最常见的真菌病害之一。随着计算机技术的飞速发展，越来越多的深度学习算法应用于白粉病菌识别。然而，现有算法存在着微小、遮挡目标识别精度低，定位精度不足的问题。针对这一问题，本文首先将并行化感知注意模块（Parallelized patch-aware attention，PPA）引入YOLO v8s的骨干网络中，通过并行的多分支结构和注意力机制，有效地捕捉了小目标的多尺度特征，在多次下采样过程中保留了关键信息，提高了小目标检测的性能。此外，将全局到局部空间聚合模块（Global-to-local spatial aggregation, GLSA）引入Neck中，通过结合全局上下文信息和局部细节特征，显著提高了模型的特征表达能力。它能够更好地捕捉多尺度特征，从而增强小目标和复杂场景中的检测性能。实验结果表明，PG-YOLO v8s对白粉病菌的检测性能较YOLO v8s大幅提升，网络对小目标和遮挡目标的检测精度显著提升，实现了高精度的白粉病菌检测。本文提供了一种高通量的白粉病菌检测方法，可在发病早期精准检测白粉病菌，指导黄瓜生产的早期智能决策。有助于提高病害防控效率，保障黄瓜产量和质量，对农业生产的可持续发展具有重要意义。

Abstract:

Cucumber is one of the most important vegetables and economic crops in the world. The occurrence of fungal diseases in cucumbers seriously threatens the safety of cucumber production, with powdery mildew being one of the most common fungal diseases. With the rapid development of computer technology, more and more deep learning algorithms are being applied to identify powdery mildew fungus. However, existing algorithms suffer from low accuracy in recognizing small and occluded targets, as well as insufficient localization precision. To address this issue, the parallelized patch-aware attention (PPA) module was firstly introduced into the backbone network of YOLO v8s. By employing a parallel multi-branch structure and attention mechanism, it effectively captured multi-scale features of small targets, preserved critical information during multiple downsampling processes, and enhanced the performance of small target detection. Additionally, the global-to-local spatial aggregation (GLSA) module was introduced into the neck, which combined global contextual information with local detail features, significantly improving the model’s feature representation capability. This module enhanced the detection performance for small targets and complex scenes by better capturing multi-scale features. Experimental results showed that PG-YOLO v8s significantly improved powdery mildew fungus detection performance compared with YOLO v8s. The network achieved high precision in detecting powdery mildew fungus, with notable improvements in the detection accuracy of small and occluded targets. The research result can provide a high-throughput method for detecting powdery mildew fungus, enabling precise early detection and guiding early intelligent decision-making in cucumber production. This approach can help to improve disease control efficiency, ensure cucumber yield and quality, and it was of great significance for the sustainable development of agricultural production.