[1]苏闪闪,吴建军,李智慧,等.轻量级网络模型在玉米病害识别与产量预测方面的研究进展[J].江苏农业学报,2025,(08):1655-1664.[doi:doi:10.3969/j.issn.1000-4440.2025.08.022]
　SU Shanshan,WU Jianjun,LI Zhihui,et al.Research progress of lightweight network models in corn disease identification and yield prediction[J].,2025,(08):1655-1664.[doi:doi:10.3969/j.issn.1000-4440.2025.08.022]
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轻量级网络模型在玉米病害识别与产量预测方面的研究进展()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年08期
页码:
1655-1664
栏目:
综述
出版日期:
2025-08-31

文章信息/Info

Title:
Research progress of lightweight network models in corn disease identification and yield prediction
作者:
苏闪闪1; 2; 吴建军1; 2; 李智慧1; 2; 甄彤1; 2
(1.河南工业大学信息科学与工程学院,河南郑州450001；2.河南工业大学粮食信息处理与控制教育部重点实验室,河南郑州450001)
Author(s):
SU Shanshan1; 2; WU Jianjun1; 2; LI Zhihui1; 2; ZHEN Tong1; 2
(1.College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China；2.Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China)
关键词:
轻量级网络模型; 玉米病害识别; 产量预测
Keywords:
lightweight network models; corn disease identification; yield prediction
分类号:
TP391；S435.131
DOI:
doi:10.3969/j.issn.1000-4440.2025.08.022
文献标志码:
A
摘要:
随着智慧农业的发展,轻量级网络模型因其计算效率高、模型参数少等优势,在资源受限的农业环境中显示出巨大潜力。本文从轻量级网络模型的定义、特点及主流模型的特点等方面对近年来轻量级网络模型在玉米病害识别与产量预测方面的研究进行综述,详细分析了轻量级网络模型在玉米病害识别和产量预测中的实际应用效果,同时指出其在实际应用中面临的挑战,结合现有研究基础提出了针对性的解决方向,旨在帮助研究人员快速、系统了解该领域的相关研究成果,推动轻量级网络模型在农业领域的广泛应用,促进智慧农业的进一步发展。
Abstract:
With the development of smart agriculture, lightweight network models have shown great potential in resource-constrained agricultural environments due to their advantages such as high computational efficiency and few model parameters. This paper reviews the research on lightweight network models in corn disease identification and yield prediction in recent years, covering aspects such as the definition and characteristics of lightweight network models and features of mainstream models. It analyzes in detail the practical application effects of lightweight network models in corn disease identification and yield prediction, and points out the challenges in their implementation. Based on existing research, targeted solutions are proposed. The purpose is to help researchers quickly and systematically understand the relevant research findings in this field, promote the wide application of lightweight network models in the agricultural field, and facilitate the further development of smart agriculture.

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备注/Memo

备注/Memo:
收稿日期：2025-03-17基金项目：国家重点研发计划项目(2018YFD0401404)作者简介：苏闪闪(1999-),女,河南商丘人,硕士研究生,研究方向为农业病害识别与深度学习。(E-mail)3087920853@qq.com通讯作者：吴建军,(E-mail)13939003632@163.com
更新日期/Last Update: 2025-09-23