Institute of Information Science, Academia Sinica, Taipei, Taiwan.
Entomology Department, National Chung Hsing University, 145 Xingda Road, South Dist., Taichung City 402204, Taiwan.
J Econ Entomol. 2021 Dec 6;114(6):2452-2459. doi: 10.1093/jee/toab162.
Several species of drywood termites, subterranean termites, and fungus-growing termites cause extensive economic losses annually worldwide. Because no universal method is available for controlling all termites, correct species identification is crucial for termite management. Despite deep neural network technologies' promising performance in pest recognition, a method for automatic termite recognition remains lacking. To develop an automated deep learning classifier for termite image recognition suitable for mobile applications, we used smartphones to acquire 18,000 original images each of four termite pest species: Kalotermitidae: Cryptotermes domesticus (Haviland); Rhinotermitidae: Coptotermes formosanus Shiraki and Reticulitermes flaviceps (Oshima); and Termitidae: Odontotermes formosanus (Shiraki). Each original image included multiple individuals, and we applied five image segmentation techniques for capturing individual termites. We used 24,000 individual-termite images (4 species × 2 castes × 3 groups × 1,000 images) for model development and testing. We implemented a termite classification system by using a deep learning-based model, MobileNetV2. Our models achieved high accuracy scores of 0.947, 0.946, and 0.929 for identifying soldiers, workers, and both castes, respectively, which is not significantly different from human expert performance. We further applied image augmentation techniques, including geometrical transformations and intensity transformations, to individual-termite images. The results revealed that the same classification accuracy can be achieved by using 1,000 augmented images derived from only 200 individual-termite images, thus facilitating further model development on the basis of many fewer original images. Our image-based identification system can enable the selection of termite control tools for pest management professionals or homeowners.
几种干木白蚁、土白蚁和菌食性白蚁每年在全球范围内造成广泛的经济损失。由于没有通用的方法可以控制所有的白蚁，因此正确的物种鉴定对于白蚁管理至关重要。尽管深度神经网络技术在害虫识别方面表现出了很大的潜力，但仍缺乏一种自动识别白蚁的方法。为了开发一种适合移动应用的自动深度学习分类器，用于白蚁图像识别，我们使用智能手机获取了四种白蚁害虫的 18000 张原始图像，包括：Kalotermitidae：Cryptotermes domesticus (Haviland)；Rhinotermitidae：Coptotermes formosanus Shiraki 和 Reticulitermes flaviceps (Oshima)；Termitidae：Odontotermes formosanus (Shiraki)。每张原始图像都包含多个个体，我们应用了五种图像分割技术来捕捉单个白蚁。我们使用了 24000 张个体白蚁图像（4 个物种×2 个性别×3 个群体×1000 张图像）进行模型开发和测试。我们使用基于深度学习的模型 MobileNetV2 实现了白蚁分类系统。我们的模型在识别兵蚁、工蚁和两种性别时，分别取得了 0.947、0.946 和 0.929 的高准确率，与人类专家的表现没有显著差异。我们进一步应用了图像增强技术，包括几何变换和强度变换，对个体白蚁图像进行了处理。结果表明，仅使用 200 张个体白蚁图像生成的 1000 张增强图像就可以达到相同的分类准确率，从而在使用更少的原始图像的基础上进一步开发模型。我们的基于图像的识别系统可以为害虫管理专业人员或房主选择白蚁控制工具提供便利。
