Abstract: This project deals with a new type of early detection of pests system. Images of the leaves affected by pests are acquired by using a digital camera. The leaves with pest images are processed for getting a gray colored image and then using image segmentation, image classification techniques to detect pests on leaves. The image is transferred to the analysis algorithm to report the quality. The technique evolved in this system is both image processing and soft computing. The image processing technique is used to detect the pests and soft computing technique is used for doing this detection over a wide population. The images are acquired by using a digital camera of approximately 12 M-Pixel resolution in 24-bits color resolution. The images are then transferred to a PC and represented in MATLAB software. The RGB image is then segmented using blob like algorithm for segmentation of pests on leaves. The segmented leave part is now analyzed for estimating pest density in field. The Support Vector Machine classifier is used to classify the pest types. It is also implemented in FPGA kit by converting the MATLAB coding into HDL coder. In FPGA, the input image is downloaded to the memory. It reads the image from memory, process it and display the output image on monitor.
Keywords: Blob like Segmentation, Classification, Early detection of pest, Image Processing, Support Vector Machine.
Title: Early Detection of Pests on Leaves Using Support Vector Machine
Author: M.Manoja, Mrs.J.Rajalakshmi
International Journal of Electrical and Electronics Research
ISSN 2348-6988 (online)
Research Publish Journals
