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.

蝗虫是世界各地的农业害虫。为了认识蝗虫的分布密度和群落结构如何与其栖息地的热液和植被生长状况相关联，从而为蝗虫入侵提供快速而准确的预警，开发有效而准确的蝗虫信息获取技术非常重要。本文通过分析南方蝗和亚洲俄底乌龟形态特征的差异，我们提出了一种基于蝗虫图像分割，蝗虫特征变量提取和支持向量机（SVM）分类的半自动蝗虫种类和幼龄信息检测模型。然后，我们根据现场获得的样本图像数据检查了其适用性和准确性。蝗虫图像分割实验表明，提出的基于GrabCut的交互式分割方法可用于快速提取蝗虫身体各个部位的图像，并且具有出色的可操作性。在蝗虫特征变量提取实验中，计算了蝗虫身体各个部位的质地，颜色和形态特征。根据结果​​，使用异常值检测，变量函数计算和主成分分析，选择了八个特征变量来识别蝗虫物种和and龄。c为2040，伽玛参数g为0.5。所提出的检测模型用于鉴定偏头痛L. migratoria manilensis和O. decorus asiaticus的蝗虫幼虫，具有很高的适用性和准确性，也可以用于鉴定其他蝗虫种类。