## 2 Experimental Data Set  
In order to ensure the performance of identifying insect pests in the actual environment, the image data used in this paper comes in two ways. The first way is that we investigated the public data set of insect pests, e.g., IP102 [24]. We re-select high-quality pictures from these data sets as a part of the final data set. Another part of the data in this paper relies on the Internet of Things monitoring system of agricultural and forestry pests. The collection of pest image data is obtained through different image acquisition cameras and equipment in IoTs, including unmanned aerial vehicles (UAVs), overhead surveillance cameras, picking equipment, pinking robots, and smartphones, which are shown in Figure 1. Afterward, these data are transmitted to the background cloud server through communication forms such as CAN/4G/WIFI. After data storage, data processing, and model learning training, the results of fine-grained identification of pests and prevention recommendations are returned in a timely manner, which can more accurately reflect the dynamic situation of the disaster. The constructed image database and intelligence models in cloud servers will provide in-time identification and decision-making services for different equipment and devices based on their practical usage requirements. For example, in smart greenhouse manage  
Figure 1: Image acquisition cameras and equipment. ment, various cameras are connected in wireless and wired communication technology to form the Internet of Things monitoring system. Some surveillance cameras fixed on the roof and column will capture the large-scope situations of crops growth and pest distribution in different viewpoints. At the same time, the smartphone can quickly obtain the detailed health status of some specific plants or even close-up photos of a single pest. Other images from other automatic farming robots or UAVs can also be transferred into the cloud database, which calls for the online image procession and analysis to complete real-time agricultural operations. Thereby, those images taken by vast devices and cameras are different in resolution, size, focal length, etc. It is necessary to gather multi-source images to train the identification models for improving their generalization and extending applicable scenarios. In view of the regional and seasonal distribution of different types of insect pests, the image collection of the pest samples in this article has been carried out in different periods from July 2018 to December 2020. A total of 74,437 effective pest images were collected in this work. Considering the different focal lengths, angles, and apertures of different camera equipment, the resolution of each photo was limited to 1000 \(\times\) 1000 pixels. The constructed data set comes from leaves, flower, fruit, stem, and other plant parts of a total of 13 plant species (apple, cherry, corn, grape, citrus, peach, pepper, potato, strawberry, tomato, wolfberry, wheat, rice). Among them, a total of 132 insect pests come from 77 families, including Plattidae, Miridae, Cantharidae, Nolidae, Corduliidae, Limacodidae, Pterophoridae, Rhopalidae, Attevidae, Tettigoniidae, Crambidae, Spodoptera frugiperda, and so on, which are shown in Figure 2.  
Considering the different occurrences of insect pests, the type distribution of various samples obtained in this paper is extremely uneven. The distribution of image data of each pest in the data set is shown in Figure 3. The columns are the number of each pest category, which has at least 100 image samples, which guarantees that all categories have the basic data amount that satisfies the model training. The largest number of samples is the 54 th class Erebidae, with a total of 5109 valid images. The least number of samples is the 1 st class Blattidae, with a total of 100 valid images.