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Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review

219
Citations
February 18, 2025
Published Date

Research Abstract & Technology Focus

Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.
advances learning applications plant disease detection
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Frequently Asked Questions (FAQ)

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What is the core focus of the research titled 'Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review'?

This literature focuses on: Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challen...

Are there open-source GitHub repositories related to Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

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What other academic literature is closely related to 'Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review'?

Yes, highly correlated activity was mapped. An entry titled 'Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review' discusses this: Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, makin...

Are there commercial applications of 'Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'PlantCLR: contrastive self-supervised pretraining for generalizable plant disease detection' discusses this: Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-bas...

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