Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon-YOLO (L-YOLO) is proposed to improve the accuracy and real-time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L-YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L-YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits. 1 INTRODUCTION Picking fruits and vegetables is a crucial part in agricultural production. However, most of it still relies on manual work currently, which consumes plenty of manpower and time, resulting in high production costs [1]. To achieve auto-picking by robots, detecting objects precisely is the primary issue. Comparing with manual lemon picking, it is of great application value and practical significance to study lemon detection in the natural environment by machine vision to promote the development of picking fruits by robots. Fruit detection is the basis of automatic picking. Many researchers have proposed some detection algorithms based on traditional image recognition methods, which have fulfilled the task of recognizing the fruits in the natural environment. Kurtulmus et al. [2] used colour, circular Gabor texture analysis and a novel 'eigenfruit' approach to detect green citrus fruits. 75.3% of the actual fruits in validation set were successfully detected by this method. Xu et al. [3] proposed a new method based on This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Silicone-epoxy polymers (SEP) with different n(Si H)/n(C C) ratios were synthesized through the hydrosilylation reaction of diallyl bisphenol A epoxy resin (DADGEBA) and hydrogen-containing silicone oil (HS) with long silicone chain.The structure of SEP was characterized by Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy ( 1 H NMR), and gel permeation chromatography (GPC), and the compatibility of the curing system was studied by dynamic mechanical analysis (DMA) and scanning electron microscope (SEM). DMA and SEM results showed that after curing with epoxy curing agent, the compatibility of the curing system was excellent. The mechanical, bonding, and thermal properties of the cured product were also measured. The cured product exhibited superior thermal stability and mechanical properties. High toughness of SEP was provided by the long silicone chain, and high mechanical strength was provided by the cross-linking networks. The maximum tensile strength was 15.64 MPa, the maximum elongation at break was 62%, and the maximum tensile lap-shear strength was 8.89 MPa. The silicone-epoxy polymers can be widely used in electronic packaging in the future.
We adopt cellular automata, a kind of complex system modeling method which achieves macro-emergence through local transition rules, to build a diffusion model. We realize parallel computation of the model with MATLAB's matrix processing and develop a GUI for model running. Through adjusting the diffusion parameters, diffusion pattern parameters, diffusion control parameters with the GUI, we achieve dynamic simulation of various diffusions' spatial-temporal changing. Apply this model to simulate air pollution diffusing and visualize simulation results in GIS. The results show that the model can not only dynamically simulate diffusion models, but also simulate diffusion under unsteady atmosphere (such as changed wind direction and speed or turbulent flow) by reasonable parameter settings. With accurate dynamic simulation of spatial-temporal distribution of pollution the model is supposed to be feasible and reasonable, and is expected to be used to simulate and predict the various diffusion patterns.
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