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Found 5 papers, 2 papers with code
Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities
We further provide several directions for future, such as creating challenge-oriented datasets and the ultimate objective deploying deep learning in real-world applications with satisfactory performance.
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
These models are trained on a large amount of data from multiple domains and modalities.
Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning
We believe that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications.
Variation-Aware Semantic Image Synthesis
Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years.
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning
In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy.
