A Review of CNN Applications in Smart Agriculture Using Multimodal Data

Abstract

This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along their publication links is made available (https://github.com/MohammadElSakka/CNN_in_AGRI).

Plant phenotyping relevance

CNNによる農業画像解析手法を、病害検出・収量予測など植物の状態や形質推定への応用として体系的にレビューしており、フェノタイピング関連手法のレビューが中心である。

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