AI RESEARCH
AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification
arXiv CS.LG
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ArXi:2603.13354v1 Announce Type: cross Reliable crop disease detection requires models that perform consistently across diverse acquisition conditions, yet existing evaluations often focus on single architectural families or lab-generated datasets. This work presents a systematic empirical comparison of three model paradigms for fine-grained crop disease classification: Convolutional Neural Networks (CNNs), contrastive Vision-Language Models (VLMs), and generative VLMs. To enable controlled analysis of domain effects, we.