FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
Researchers have developed FruitEnsemble, a novel framework for fine-grained fruit classification that addresses challenges like limited datasets and visual similarity between fruit types. The system utilizes a two-stage approach, beginning with a weighted ensemble of different models to create a candidate pool. For difficult cases, a multimodal large language model (MLLM) is employed to verify classifications by cross-referencing botanical descriptions with Chain-of-Thought reasoning, achieving a 70.49% accuracy rate. AI
IMPACT Enhances agricultural computer vision by improving the accuracy and efficiency of fruit classification for sorting and quality inspection.