A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

IF 2.3 3区 农林科学 Q1 AGRONOMY
Burak Gülmez
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Abstract

This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.

Abstract Image

基于卷积神经网络的马铃薯农业病害检测策略综述
本综述论文研究了卷积神经网络(CNN)在马铃薯农业病害检测中的应用,强调了其在有效分析大规模农业数据集方面的关键作用。论文仔细研究了所使用的数据集、预处理方法、特定的数据收集区域,以及 ResNet、VGG 和 MobileNet 等著名算法在病害分类方面的功效。此外,还研究了网格搜索、随机搜索、遗传算法和贝叶斯优化等各种超参数优化技术,并评估了它们对模型性能的影响。讨论部分探讨了数据集稀缺性、疾病症状的可变性以及模型在不同环境条件下的泛化等挑战。此外,还探讨了推进基于 CNN 的疾病检测的机遇,包括多光谱成像和遥感数据的整合,以及用于协作模型训练的联合学习的实施。未来的发展方向是研究稳健的迁移学习技术,以及在实时监测系统中部署 CNN,以便在马铃薯农业中进行前瞻性疾病管理。本综述整合了当前的知识,确定了研究空白,并提出了基于 CNN 的病害检测策略的未来研究方向,以有效维持马铃薯种植业。本研究为人工智能驱动的疾病检测的未来发展铺平了道路,有可能彻底改变农业实践并提高粮食安全。此外,本研究还旨在指导未来在基于 CNN 的马铃薯农业病害检测方面的研发工作,从而有可能改进作物管理方法、提高产量和加强粮食安全。
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来源期刊
Potato Research
Potato Research AGRONOMY-
CiteScore
5.50
自引率
6.90%
发文量
66
审稿时长
>12 weeks
期刊介绍: Potato Research, the journal of the European Association for Potato Research (EAPR), promotes the exchange of information on all aspects of this fast-evolving global industry. It offers the latest developments in innovative research to scientists active in potato research. The journal includes authoritative coverage of new scientific developments, publishing original research and review papers on such topics as: Molecular sciences; Breeding; Physiology; Pathology; Nematology; Virology; Agronomy; Engineering and Utilization.
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