摘要
智慧农业作为农业现代化发展的核心方向,正在逐步引入人工智能技术以提升生产效率、保障粮食安全、推动可持续发展。其中,深度学习作为人工智能领域的前沿技术,凭借其强大的特征提取和数据建模能力,在农业感知、预测和决策等方面表现出显著优势。基于此,系统梳理深度学习的基本原理及其在智慧农业中的关键应用场景,重点分析其在作物识别与分类、病虫害检测、产量预测与环境建模、精准灌溉与施肥等方面的应用现状和技术路径,探讨深度学习模型的适用性、数据需求及未来发展趋势,为农业智能化转型提供理论依据。
As a core direction in the modernization of agriculture,smart agriculture is increasingly incorporating artificial intelligence technologies to enhance production efficiency,ensure food security,and promote sustainable development.Among these technologies,as an advanced branch of artificial intelligence,deep learning has demonstrated significant advantages in agricultural sensing,prediction,and decision-making,owing to its powerful feature extraction and data modeling capabilities.This paper systematically reviews the fundamental principles of deep learning and its key application scenarios in smart agriculture.It focuses on current applications and technical approaches in crop identification and classification,pest and disease detection,yield prediction and environmental modeling,as well as precision irrigation and fertilization.Furthermore,the study explores the applicability of deep learning models,their data requirements,and future development trends,aiming to provide theoretical support and practical guidance for the intelligent transformation of agriculture.
作者
柳卫斌
LIU Weibin(Gansu Zhong Dixin Remote Sensing Technology Co.,Ltd.,Lanzhou 730070,China)
出处
《智能物联技术》
2025年第4期21-25,共5页
Technology of Io T& AI
关键词
智慧农业
深度学习
遥感信息提取
农业感知
smart agriculture
deep learning
remote sensing information extraction
agricultural perception
