A cosine-consistency voting scheme with a domain-adapted embedding raises VLM accuracy on maize disease diagnosis by 5.6 to 15.5 percentage points, but the gain is measured with the same LLM scorer used to create the training labels.
Central to this trans- formation is the automated analysis of agricultural images for real-time crop monitoring, disease detection, and treatment recommendation
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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management
A cosine-consistency voting scheme with a domain-adapted embedding raises VLM accuracy on maize disease diagnosis by 5.6 to 15.5 percentage points, but the gain is measured with the same LLM scorer used to create the training labels.