Development of agricultural pests detection and identification system based on the technology of wireless transmission - Song Gelian; Han Ruizhen; Zhang Yonghua; He Yong (浙江大学学报. 农业与生命科学版, 2014)
The automatic remote pest-identification system is developed aiming at solving the difficulty of detecting pests in good timing in farmland, which applies modern wireless image and data transaction tools. The system consists of one main controller and several supplemental remote platforms. In structure, every single platform includes function modules like image acquisition, image processing, feature extraction and classification, GPS and transmission module. These platforms communicate with each other through 3G/4G wireless network. Host end and the remote end has access to photos capture, pictures reading, feature extraction, feature selection, expert identification, pictures sending and so on.The workflow is as follows: 1) the remote platforms capture the images of stationary pests with image acquisition system; 2) the system extracts certain pests from the farming background according to the constructed vector including 16 morphological characteristic values such as area, perimeter, complexity, eccentricity and invariant moment, 9 colors eigenvalues and 10 texture constituted feature; 3) finally, the system classifies pests into 7 major types which are Cnaphalocrocis medinalis Guenee, Prodenia litura, Chilo sup pressalis, Ostrinia nubilalis, Naranga aenesc, Sogatella furcifera and Agrotis y psilon Rottemberg with Otsu threshold segmentation method based on HSV color mode. The accuracy of this technology was proved to be 88.5%.The identification process can be completed in both remote platform and in the host control platform automatically after the pest images were compressed and transmitted to the host control platform through 3G/4G wireless network. With more and more application and information collected, the system will expand the sample library dynamically by saving the image into the local disks.The feasibility of the system is discussed and statistically significantly tested in the context. The advent of the automatic remote pest-identification system could help provide pest information about the farmland timely and accurately thus improving the prevention effect.