Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-07-12 DOI:10.1007/s12652-024-04832-9
Shreya Thiagarajan, A. Vijayalakshmi, G. Hannah Grace
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Abstract

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow.

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精准农业中的杂草检测:利用编码器-解码器模型进行语义分割
精准农业利用从各种来源收集的数据来提高农业产量和作物管理技术(如施肥、灌溉管理和杀虫剂施用)的有效性。减少农用化学品的使用是实现更可持续农业的关键一步。杂草管理机器人可以执行选择性洒水或机械除草等任务,有助于实现这一目标。要让这些机器人发挥作用,就必须有一个值得信赖的作物/杂草分类系统,能够对作物和杂草进行准确识别和分类。在本文中,我们探索了各种深度学习模型,以便在更短的训练时间内获得可靠的分割结果。我们使用语义分割模型将图像的每个像素划分为不同类别。这些模型基于编码器-解码器架构,在编码过程中提取特征图,在解码过程中恢复空间信息。我们在一个包含不同杂草的豆类数据集上检验了分割输出,这些杂草是在非常不同的环境条件下采集的,包括阴天、雨天、黎明、傍晚、阳光充足和阴影。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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