ISSN : 2319-7323

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE ENGINEERING

ABSTRACT

Title	:	Detection of the affected area and classification of pests using convolutional neural networks from the leaf images
Authors	:	Alagiah Suthakaran, Saminda Premaratne
Keywords	:	segmentation, the region of interest, pest detection, convolution neural networks.
Issue Date	:	Jan-Feb 2020
Abstract	:	Pest infection is the most crucial problem in vegetable plants. One way to control pest infection is to use proper pesticides. Early detection of the pest or the initial presence of pests is a key element for crop protection. The identification of the pest was made manually at the beginning. This takes time and also requires ongoing monitoring of experts. An automatic pest detection system is needed to examine the infestation and classify the type of pest. Today, there are many techniques and methods for identifying pests and detecting plant diseases. In these techniques, image processing techniques are very efficient and reliable. First, the proposed model detects whether the leaf is affected or not and calculates the affected area in the image. Next, the region of the detected pest and classification were performed using convolutional neural networks. The severity of the infection can be observed by calculating the percentage of the affected area, which leads to taking the appropriate measures.
Page(s)	:	1-10
ISSN	:	2319-7323
Source	:	Vol. 9, No. 1