Using an image segmentation and support vector machine method for identifying two locust species and instars

• Published in: Journal of Integrative Agriculture Vol. 19; no. 5; pp. 1301 - 1313
• Main Authors: LU, Shuhan, YE, Si-jing
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020; Department of Computer and Information Science, College of Art and Science, Ohio State University, Columbus, OH 43210, USA%State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, P.R.China; Center for Geodata and Analysis, Beijing Normal University, Beijing 100875, P.R.China; Elsevier
• Subjects: L. migratoria manilensis; locust identification; machine learning; O. decorus asiaticus; support vector machine; L. migratoria manilensis; support vector machine; machine learning; O. decorus asiaticus; locust identification; O.decorus asiaticus; L.migratoria manilensis
• DOI: 10.1016/S2095-3119(19)62865-0
• ISSN: 2095-3119

Locusts are agricultural pests around the world. To cognize how locust distribution density and community structure are related to the hydrothermal and vegetation growth conditions of their habitats and thereby providing rapid and accurate warning of locust invasions, it is important to develop efficient and accurate techniques for acquiring locust information. In this paper, by analyzing the differences between the morphological features of Locusta migratoria manilensis and Oedaleus decorus asiaticus, we proposed a semi-automatic locust species and instar information detection model based on locust image segmentation, locust feature variable extraction and support vector machine (SVM) classification. And we subsequently examined its applicability and accuracy based on sample image data acquired in the field. Locust image segmentation experiment showed that the proposed GrabCut-based interactive segmentation method can be used to rapidly extract images of various locust body parts and exhibits excellent operability. In a locust feature variable extraction experiment, the textural, color and morphological features of various locust body parts were calculated. Based on the results, eight feature variables were selected to identify locust species and instars using outlier detection, variable function calculation and principal component analysis. An SVM-based locust classification experiment achieved a semi-automatic detection accuracy of 96.16% when a polynomial kernel function with a penalty factor parameter c of 2 040 and a gamma parameter g of 0.5 was used. The proposed detection model exhibits advantages such as high applicability and accuracy when it is used to identify locust instars of L. migratoria manilensis and O. decorus asiaticus, and it can also be used to identify other species of locusts.