Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review.

Abstract

The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.

Plant phenotyping relevance

植物病害を画像から検出・分類する深層学習およびIoT手法を体系的にレビューし、モデル性能比較も行っているため、植物フェノタイピング手法レビューとして中心的です。

Code and data availability

This is a review article on IoT and deep learning for plant disease detection. The supplied blocks contain no authors' phenotype datasets, images, code, models, or supplements with availability statements; all datasets and models mentioned (e.g., PlantVillage, AlexNet, ResNet) are cited prior work or generic references

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