A new survey paper details the application of deep learning techniques, including vision transformers and vision-language models like CLIP, to various agricultural tasks. The research covers crop disease detection, livestock health management, and aquatic species monitoring, while also addressing implementation challenges such as data variability and experimental metrics. The paper highlights future research directions, emphasizing multimodal data integration and efficient deployment on edge devices for diverse farming environments. AI
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IMPACT This survey highlights the growing application of advanced AI techniques in agriculture, potentially improving efficiency and sustainability in food production.
RANK_REASON This is a survey paper published on arXiv detailing AI techniques in agriculture.
- CLIP
- AI
- Deep Learning
- Vision Transformers
- Agriculture
- Crops
- Fisheries
- Livestock
- Machine Learning
- Food Production
- Climate Variability
- Resource Limitations
- Sustainable Management
- Crop Disease Detection
- Livestock Health Management
- Aquatic Species Monitoring
- Data Variability
- Edge-Device Deployment
- Multimodal Data Integration