Agricultural pest catches on sticky traps can be used for the early detection and identification of hotspots, as well as for estimating relative abundances of adult pests, occurring in greenhouses. This study aimed to construct a detection model for whitefly and thrips from sticky trap images acquired in greenhouse conditions. An end-to-end model, based on the Faster regional-convolutional neural network (R-CNN), termed ‘TPest-RCNN’, was developed to improve the tiny pest detection accuracy. This architecture was trained using a transfer learning strategy on the Common Objects in Context dataset before training on the tiny pest training set to create the TPest-RCNN model. The new model achieved mean F1 score and average precision of 0.944 and 0.952, respectively, on a validation set. The TPest-RCNN model outperformed the Faster R-CNN architecture and other approaches using handcrafted features (color, shape and/or texture) in detecting multiple species from yellow sticky trap images. The test results also showed the model was robust to detect tiny pests on images of different pest densities and light reflections. Using a linear regression between the manual counts and an automatic detection results using the proposed method on images of 41 days, the determination coefficients reached 99.6% and 97.4% for whitefly and thrips, respectively. These results demonstrated that the proposed method could facilitate rapid gathering of information pertaining to numbers of the abundance of tiny pests in greenhouse agriculture and provide a technical reference for pest monitoring and population estimation.

粘性诱集装置上的农业害虫捕获物可用于早期发现和识别热点，以及估计温室中发生的成年害虫的相对丰度。这项研究旨在从温室条件下获取的粘性陷阱图像构建粉虱和蓟马的检测模型。建立了一个基于快速区域卷积神经网络（R-CNN）的端到端模型，称为“ TPest-RCNN ”，以提高微小虫害的检测精度。在对小型害虫训练集进行训练以创建TPest-RCNN之前，对上下文数据集中的公共对象使用转移学习策略对这种体系结构进行了训练。模型。新模型在验证集上分别获得了F1平均值和0.944和0.952的平均精度。该TPest-RCNN该模型在从黄色粘性陷阱图像中检测多种物种方面，优于手工制造的特征（颜色，形状和/或纹理），胜过Faster R-CNN体系结构和其他方法。测试结果还表明，该模型对于检测不同虫害密度和光反射图像上的微小虫害具有鲁棒性。在41天的图像上，使用人工计数和使用建议的方法的自动检测结果之间的线性回归，粉虱和蓟马的测定系数分别达到了99.6％和97.4％。这些结果表明，所提出的方法可以促进温室农业中大量微小害虫数量信息的快速收集，并为害虫监测和种群估计提供技术参考。