Overview of Image Processing Approach for Identification of Agricultural Pests
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 insects.In vidarbha(Maharashtra) region, mostly in kharif period crops like cotton, soyabean, pigeonpea are grown and in rabi chickpea crop grown. Most common pod borer (Helicoverpa armigera) pest attacks cotton, pigeonpea, chickpea,sunflower,tomato and other crops. according to survey, minumum 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 is done for identification of whitefly pest on sticky traps in greenhouse environments and in real fields. We propose a desion support system which exposes a 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 cotton, pigeonpea,chickpea crops .after that captured image would be preprocessed for enhancement. Then segmentation will be carried out with help of OTSU thresholding.using segmentation we would be segmenting out pest from forground. After segmentation, various features of pests including color and shape will be extracted. This extracted features will be stored in database with name of pest. With the help of SVM classifier, we would be finding out the type of pest and give preventive and control measures to user.