利用图像处理技术检测树叶上昆虫入侵症状

Muhammad Badrisya Nordin, S. B. Hisham
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引用次数: 0

摘要

该项目旨在帮助霹雳州卢穆特的农民对抗蓟马对芒果树的入侵。这将有助于减少果树生产分支的损失,减少人工检查,减少人工覆盖大片土地的需要。使用佳能数码单反相机在自然光线和无控制背景下拍摄。健康和患病新叶的图像经过预处理以去除噪声。掩蔽和阈值使用范围的强度值被用来去除背景。然后,使用模糊c均值聚类对图像进行聚类。由于该方法采用了软聚类方法,因此比K-Means聚类更适合。然后使用支持向量机(SVM)对得到的图像进行分类。平均分类准确率为9S。达到52%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Insect Invasion Symptoms on Tree Leaves Using Image Processing
This project aims to help farmers in Lumut, Perak to combat thrips invasion on mango trees. It would help reduce loss of fruit-producing branches, manual inspections, and the need to cover large acres of land manually. Data was collected by using a Canon DSLR camera at lm distance in natural lighting and uncontrolled background. Images of healthy and diseased new leaves are pre-processed to remove noise. Masking and thresholding using a range of intensity values are used to remove the background. After that, the images were clustered using Fuzzy C-Means clustering. It was found that this method was more suitable than K-Means clustering as it uses a soft clustering approach. The images obtained were then classified using Support Vector Machine (SVM). An average classification accuracy of 9S.52% was achieved.
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