Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes  
[PERSON]  
1 College of Engineering, Beijing Forestry University, Beijing 100086, China; [EMAIL_ADDRESS] (K.M. ); [EMAIL_ADDRESS] (J.L.) [PERSON]  
2 Business Growth Business-Unit, Jing Dong, Beijing 100176, China; [EMAIL_ADDRESS]  
[PERSON]  
and preventing large-scale disasters, which are enabled to improve the production, quality, and supply of various agricultural and forestry production, as well as improve the management efficiency of the entire industrial chain [4]. With the significant advancements of artificial intelligence and sensing technologies, including Internet of Things (IoT), unmanned aerial vehicles (UAVs), automatic robots, cloud computing analysis, and 5G communication, the \"smart agriculture & forestry\" has already become the modern development trend in recent years through the full chain of real-time information perception, quantitative decision-making, intelligent production control, and precise personality management [5]. In terms of insect pest identification, farmers and researchers have already applied various information and intelligent technologies to improve the working performance of complicated produce tasks in practical smart farming [6; 7]. With high-quality image data of insect pests acquired by various surveillance cameras and vision sensors, many machine-learning methods, including local binary patterns [8], support vector machine [9], fuzzy set, BP neural network [10], etc. have been applied to discriminate insect pest varieties. However, those classical methods mainly rely heavily on complex statical analysis and designed feature engineering to gain a modest performance. This process usually requires lots of time-consuming and hand operations in a subjective manner to tweak plenty of parameters for reaching a modest level of identification accuracy. What is worse, existing methods are trained on the limited plant data sets collected in a controlled laboratory environment, which cannot deal with the practical applications of pest identification in a natural agricultural and forestry context. In recent decades, deep-learning technology has made great development in visual applications, including image classification, object detection, and other video caption, which have been a very promising candidate for a practical and generalizability solution for various agricultural and forestry tasks [11; 12]. Illuminated by the multilevel brain's perception structure of human vision, deep-learning neural networks design a computing model composed of multiple processing layers and nonlinear activation modules so that it can automatically learn the higher-dimensional representation from large-scale training samples at a more abstract and general level. Moreover, with the comprehensive guidance of optimization strategies and various learning tricks, deep-learning neural networks (DNN) could achieve better performance surpassing human recognition or traditional methods on different visual recognition aspects [13; 14; 15]. At present, several deep-learning models have been used in the image recognition of pest species, which obtained the better or even the best results in different forestry and agricultural scenes. For example, a DNN-based classification framework based on the convolutional neural networks (CNN) was implemented to recognize insect pest invasion situation on isolated plant leaf [16; 17]. Several deep-learning neural networks, such as VGG16 [18], ResNet [19], and Inception [20], have also been applied to classify pest species and achieved considerable performance. In order to guarantee the amount of training data for training complicated deep-learning models, many studies have collected a large number of public data sets of plant pest categories. [PERSON] et al. [21] collected an image data set named the PlantVillage, which contains 14 crop types and 26 pest categories with over 50,000 images.