Research on Bursapherenchus Xylophophilus Disease Recognition Based on HSV Space

Xuejiao Luo, Yage Deng, Yunxia Hu, Fangfang Xu, Tong Ye
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引用次数: 0

Abstract

This study aims at the shortcomings of traditional artificial ground detection methods for Bursapherenchus xylophophilus disease and applies HSV(Hue-Saturation-Value) color model to realize automatic identification of Bursapherenchus xylophophilus disease and determine its degree of disaster.The entire process is divided into forest data collection, image processing, nematode disease identification and grade determination.The study conducted repeated comparisons and adjusted HSV threshold tests to obtain the HSV threshold with optimal recognition results, and then identify Bursaphelenchus xylophophilus disease and calculate its disease severity. This method is simple to operate and has good identification effects. It can also effectively improve the accuracy and efficiency of pine wood nematode diagnosis. It can be widely used in the field of agriculture and forestry to help better complete disease detection and carry out prevention and control measures more accurately, thereby effectively Protect forest natural resources and improve forestry production efficiency.
基于 HSV 空间的 Xylophilus Bursapherenchus 疾病识别研究
本研究针对传统人工地表检测嗜木毛虫病方法的不足,应用 HSV(色相-饱和度-色值)色彩模型实现对嗜木毛虫病的自动识别,并确定其受灾程度。整个过程分为森林数据采集、图像处理、线虫病识别和等级判定等环节。该研究通过反复对比和调整 HSV 阈值试验,得到识别效果最佳的 HSV 阈值,进而识别嗜木毛囊虫病害并计算其病害严重程度。该方法操作简单,识别效果好。它还能有效提高松材线虫诊断的准确性和效率。它可广泛应用于农林领域,帮助更好地完成病害检测,更准确地开展防治措施,从而有效保护森林自然资源,提高林业生产效率。
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CiteScore
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