Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing
| Autor: | Yang Baojun, Diao Guangqiang, Qing Yao, Jian Tang, Ding-xiang Xian, Qing-jie Liu |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
business.product_category
Computer science insect counting Agriculture (General) Image processing Plant Science Biochemistry S1-972 Food Animals Histogram Rice plant rice planthoppers handheld device Digital camera Ecology business.industry Pattern recognition image features Support vector machine Haar-like features Adaboost classifier Animal Science and Zoology Artificial intelligence Sogatella furcifera AdaBoost classifier SVM classifier business Agronomy and Crop Science Food Science |
| Zdroj: | Journal of Integrative Agriculture, Vol 13, Iss 8, Pp 1736-1745 (2014) |
| ISSN: | 2095-3119 |
| DOI: | 10.1016/s2095-3119(14)60799-1 |
| Popis: | A quantitative survey of rice planthoppers in paddy fields is important to assess the population density and make forecasting decisions. Manual rice planthopper survey methods in paddy fields are time-consuming, fatiguing and tedious. This paper describes a handheld device for easily capturing planthopper images on rice stems and an automatic method for counting rice planthoppers based on image processing. The handheld device consists of a digital camera with WiFi, a smartphone and an extrendable pole. The surveyor can use the smartphone to control the camera, which is fixed on the front of the pole by WiFi, and to photograph planthoppers on rice stems. For the counting of planthoppers on rice stems, we adopt three layers of detection that involve the following: (a) the first layer of detection is an AdaBoost classifier based on Haar features; (b) the second layer of detection is a support vector machine (SVM) classifier based on histogram of oriented gradient (HOG) features; (c) the third layer of detection is the threshold judgment of the three features. We use this method to detect and count whiteback planthoppers ( Sogatella furcifera ) on rice plant images and achieve an 85.2% detection rate and a 9.6% false detection rate. The method is easy, rapid and accurate for the assessment of the population density of rice planthoppers in paddy fields. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect