Abstract: An accurate and rapid identification is required for the Clivia miniata pests and diseases in the greenhouse and garden in recent years. In this study, the YOLO V4-TLite algorithm was proposed to detect the Clivia miniata pest using a mobile terminal. The real-time performance and high accuracy were also achieved to reduce the over-reliance on the high-computing and high-power hardware. Firstly, the dataset of Clivia diseases and insect pests was collected from the real planting in the greenhouse. The images were captured at the early and middle stages of Clivia diseases and insect pests in winter and spring. Secondly, a low-cost improved partial convolution was used to replace the traditional one in the backbone network using the YOLO V4-Tiny model. The improved model was then obtained with the high speed of operation and the low consumption of memory. Thirdly, an improved structure of the inverse residual network was used to form a lightweight backbone network. The hardware compatibility was also enhanced to reduce the large consumption of random storage in the depth of the backbone network in the YOLO V4-Tiny model. The high operation speed of the model was obtained with the compatibility of the mobile terminal with the limited resources. Fourthly, the weight-sharing convolution was combined with the conventional convolution for channel fusion. The high robustness and accuracy of the network were obtained to reduce the redundant feature maps and their attention distraction in the traditional convolution layer of the YOLO V4-Tiny model. Finally, the improved model was deployed on the ROCK 5B mobile. Three types of Clivia miniata pests were then tested: leaf blight, maculopathy, and coccid. The experimental results showed that the better performance of the improved model was achieved with the mean average precision (mAP) of 78.5% at an intersection over union (IoU) ratio of 0.5, memory usage of only 4.8MB, and the floating point operations (FLOPs) of 1.3 G. The desktop single detection speed was 0.005 s with 70 W power consumption. On the mobile side, the CPU single detection speed was 0.239 s with 10 W power consumption. The NPU single detection speed was 0.018 s with 7 W power consumption. Compared with the original YOLO V4-Tiny model, the mAP50 of the YOLO V4-TLite model increased by 12.6 percentage point, whereas, the model size decreased by 78.6%. The computational efficiencies of the YOLO V4-TLite model were improved by 37.5 and 85.9 percentage point on the desktop and mobile side, respectively. While the power consumption demands were reduced by 26 and 2 W, respectively. The mAP50 values were 3.9, 2.3, 1.6, and 1.3 percentage point higher, respectively, compared with the target detection models of YOLOV11-N, YOLO V10-N, YOLO V7-Tiny, and YOLO V5-S. The YOLO V4-TLite model can be expected to detect the Clivia Miniata pest and disease on the low resource and power mobile. Better performance was also achieved, compared with the existing mainstream YOLO series models.