期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：3
页码：4034-4037
出版社：TechScience Publications
摘要：Plant pest identification and detection is vital for food security, quality of life and a stable agricultural economy. Enormous agricultural yield is lost every year, due to rapid infestation by pests and according to experts, minimum 10% of crop yield to pigeon pea crop is lost due to pod borer (Helicoverpa armigera) pest attacks. Various methodologies were proposed earlier for identification and detection of agriculture pests. Mostly work was done for identification of whitefly pest on sticky traps in greenhouse environments and in real fields. We propose a new approach which exposes advance computing technology that has been developed to help the farmer to identify agricultural pests and take proper decision about preventive or control measure of it. Diagnosis of agricultural pests in the field is very critical and difficult. In our proposed work, we would be capturing images of pests from various crops like cotton, pigeon pea, chickpea, etc. Agricultural pest which need to be extracted available in foreground and hence from an image foreground need to be separated. For this saliency map based segmentation will be carried out. After segmentation, various features of segmented pests will be extracted. Feature vector includes energy is calculated with the help of discrete wavelet transform. These features will be stored in database with name of pest. With the help of back propagation neural networks, we would be classifying type of pest and give preventive and control measures to user.
