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Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection

103
Citations
October 24, 2024
Published Date

Research Abstract & Technology Focus

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.
intelligent agriculture learning uav-based remote sensing
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What is the core focus of the research titled 'Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection'?

This literature focuses on: Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmann...

Are there open-source GitHub repositories related to Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection?

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 Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection?

Products like Google Gemma 4 are bringing this to market. Their focus is: Google's most intelligent open models to date.

What other academic literature is closely related to 'Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection'?

Yes, highly correlated activity was mapped. An entry titled 'Deep Learning-Based Weed Detection and Classification in Wheat Fields from UAV Imagery' discusses this: Weed infestation significantly threatens crop productivity and quality, highlighting the need for accurate and scalable monitoring approaches. Rece...

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