Classification of rice planthoppers based on shape descriptors

Abstract

Here, classification of rice planthopper (RPH) based on shape descriptors was addressed to solve the low semantics problem of shape features in traditional RPH (mainly including the whiteback planthopper (Sogatella furcifera (Horváth)), the brown planthopper (Nilaparvata lugens (Stål)), and the small brown planthopper (Laodelphax striatellus (Fallén))) image classification research. Images of RPH were obtained from rice field by an automatic insect image acquisition device made by ourselves and insect images were divided into single images based on OTSU threshold segmentation algorithm. In terms of the images of RPH after segmentation, Fourier descriptors and Hu moments, which are from two aspects of contour curve and shape area, were extracted to describe shape features of RPH. Then, random forest (RF), an ensemble learning algorithm, was used as the classifier to distinguish RPH efficiently. The optimal number of trees and prediction variables of RF are chosen to be 150 and 4, respectively, by minimising the out-of-bag error. Experimental results show that classification accuracy of RPH based on shape descriptors reaches up to 93.93%. Therefore, it has been verified that the classification with the method presented here is accurate and semantic.

Keywords

• crops
• feature extraction
• agriculture
• image segmentation
• image classification
• random forests
• edge detection
• minimisation
• contour curve
• shape area
• shape features
• classification accuracy
• shape descriptors
• rice planthopper
• low semantics problem
• whiteback planthopper
• rice field
• automatic insect image acquisition device
• insect images
• otsu threshold segmentation algorithm
• fourier descriptors