Detection of Insect Invasion Symptoms on Tree Leaves Using Image Processing

Muhammad Badrisya Nordin, S. B. Hisham
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Abstract

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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利用图像处理技术检测树叶上昆虫入侵症状
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%。
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