Zero-shot VLMs reach at most 62% accuracy on agricultural classification tasks while supervised models like YOLO11 perform markedly higher, indicating they are not ready to replace task-specific systems.
hub
Title resolution pending
2 Pith papers cite this work, alongside 4,580 external citations. Polarity classification is still indexing.
2
Pith papers citing it
4,580
external citations · OpenAlex
hub tools
verdicts
UNVERDICTED 2representative citing papers
This design-science study proposes a layered architecture, closed-loop decision process, function-pain-point matrix, evaluation indicators and governance framework for the Zhinong AI agricultural decision-support platform without providing measured field performance.
citing papers explorer
-
Are vision-language models ready to zero-shot replace supervised classification models in agriculture?
Zero-shot VLMs reach at most 62% accuracy on agricultural classification tasks while supervised models like YOLO11 perform markedly higher, indicating they are not ready to replace task-specific systems.
-
Zhinong AI: A Design-Science Study of an AI-Enabled Agricultural Decision-Support Platform for Smallholder Production
This design-science study proposes a layered architecture, closed-loop decision process, function-pain-point matrix, evaluation indicators and governance framework for the Zhinong AI agricultural decision-support platform without providing measured field performance.