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Abstract
Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.
Academic Publication Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture
Research Abstract & Technology Focus
Correlated Market Trend: Adaptive Learning
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Frequently Asked Questions (FAQ)
Curated market intelligence mapped to this research.
What is the core focus of the research titled 'Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture'?
This literature focuses on: Abstract Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts...
Are there open-source GitHub repositories related to Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture?
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.
Which startups are commercializing the technology behind Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture?
Products like Perplexity Computer are bringing this to market. Their focus is: Everything AI can do, Perplexity Computer does for you..
What other academic literature is closely related to 'Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture'?
Yes, highly correlated activity was mapped. An entry titled 'Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture' discusses this: Abstract Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food se...
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Commercial Realization
Startups and Open Source tools heavily associated with the concepts explored in this paper.
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GitHubTHU-MAIC/OpenMAIC
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GitHubfikrikarim/parlor
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Product HuntPerplexity Computer
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Product HuntComputer Use in Claude Code