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面向农业机器人应用的边缘人工智能和自适应嵌入式系统设计.pdf

上传人: 芦苇 编号:651848 2025-05-01 28页 1.81MB

1、An Edge AI and Adaptive Embedded System Design for Agricultural Robotics ApplicationsChun-Hsian Huang1,Zhi-Rui Chen2,and Huai-Shu Hsu21Dept.Electrical Engineering,National Changhua University of Education2Dept.Computer Science and Information Engineering,National Taitung UniversityOutlineIntroductio

2、nProposed methodData collection for AI modelsRecognition of target crops and their pest and disease severity(PDS)estimation using binarized neural networks(BNNs)PDS prediction using multimodal learningAgrBot designAgricultural cyber-physical system(CPS)System implementation and evaluationsConclusion

3、2OutlineIntroductionProposed methodData collection for AI modelsRecognition of target crops and their PDS estimation using BNNsPDS prediction using multimodal learningAgrBot designAgricultural CPSSystem implementation and evaluationsConclusion3IntroductionMonitoring crop pest and disease severity(PD

4、S)is crucial to ensure the healthy growth of cropsThe motivation of this work is to enable an agricultural robot to directly estimate and predict the crop PDS in the growth environment.Based on the PDS estimation and prediction,the agricultural robot can apply biological agents to protect the crops

5、from pests and diseases.4Agricultural Cyber-Physical System(CPS)Adaptive binarized neural network(BNN)hardware modulePrediction of PDS based on heterogeneous data5OutlineIntroductionProposed methodData collection for AI modelsRecognition of target crops and their PDS estimation using BNNsPDS predict

6、ion using multimodal learningAgrBot designAgricultural CPSSystem implementation and evaluationsConclusion6Data Collection for AI Models7Three Levels of PDS for Dragon Fruits8OutlineIntroductionProposed methodData collection for AI modelsRecognition of target crops and their PDS estimation using BNNs

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本文介绍了一种适用于农业机器人的人工智能和自适应嵌入式系统设计。该系统通过采用适应性二值神经网络(BNN)硬件模块和多模态学习方法,能够在作物生长环境中直接估计和预测作物病虫害严重程度(PDS)。研究首先采用VGG16架构并经过二值权重正则化来支持边缘AI的目标作物识别和PDS估计。数据预处理使用模糊粗糙集(FRS)来减少输入数据大小,同时保留更多相关信息以提高预测准确性。系统架构设计中,采用了可重构分区和基于FPGA的系统设计流程,实现了BNN硬件模块的序列使用,以估计目标作物的PDS。此外,农业机器人(AgrBot)原型采用了AVNET Ultra96-V2(FPGA)和NVIDIA Jetson Nano(GPU),显著提高了每秒帧数(FPS),实现了与微处理器方法相比的速度提升。研究表明,AgrBot在资源使用、预测准确性和性能评估方面具有优势,并可适用于多种作物,展现了其高度的性能、资源效率和可扩展性。
"如何利用AI估计作物病害严重程度?" "AgrBot如何设计以适应农业应用?" "如何通过多模态学习预测作物病害?"
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