AI 中文总结
BananaVLM基于LLaVA和LoRA，通过自动生成的80k问答对微调，在九类香蕉病害基准上实现92.21%域内和83.28%域外准确率，显著超越闭源模型，验证了轻量级领域自适应的有效性。
AI 中文摘要
香蕉作物病害威胁着全球粮食安全，然而由于专家资源有限且病害类别间视觉相似，田间诊断仍然困难。我们提出了BananaVLM，一个基于LLaVA-v1.5-7B构建并通过低秩适配（LoRA）微调的领域自适应视觉语言模型，用于香蕉病害诊断。为解决多模态农业指令数据的缺乏，我们引入了BananaInstruct，一个自动化流水线，无需人工标注即可将原始病害图像转换为约80,000个问答对，利用LLaVA-1.5-13B生成症状描述，Mistral-7B生成农业问答并进行标签校准。在包含14个开源和5个闭源视觉语言模型的九类基准评估中，BananaVLM实现了92.21%的域内和83.28%的域外（OOD）分类准确率，分别比最佳闭源基线Gemini 2.5 Pro高出49.8和63.3个百分点。二元健康/病害识别达到98.38%的OOD准确率和1.00的召回率。受控的LoRA与DoRA对比显示，DoRA性能随训练轮次下降，而LoRA在细粒度分类中保持更有效。使用九个LLM评判者的定性评估得出G-Eval胜率为0.81至0.96，而五位领域专家在990次盲法成对比较中有98.48%偏好BananaVLM。这些结果表明，轻量级领域自适应结合自动指令调优是面向专业农业AI的有效且可扩展的方法。代码、数据集和模型权重可在该https URL获取。
英文摘要
Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual similarity among disease classes. We present BananaVLM, a domain-adapted vision-language model for banana disease diagnosis built on LLaVA-v1.5-7B and fine-tuned with Low-Rank Adaptation (LoRA). To address the lack of multimodal agricultural instruction data, we introduce BananaInstruct, an automated pipeline that converts raw disease images into $\approx$80,000 question--answer pairs without manual annotation, using LLaVA-1.5-13B for symptom descriptions and Mistral-7B for agricultural Q&A generation and label grounding. Evaluated against 14 open-source and 5 closed-source VLMs on a nine-class benchmark, BananaVLM achieves 92.21% in-domain and 83.28% out-of-domain (OOD) classification accuracy, outperforming the best closed-source baseline, Gemini~2.5~Pro, by 49.8 and 63.3 percentage points, respectively. Binary healthy/diseased identification reaches 98.38% OOD accuracy with 1.00 recall. A controlled LoRA--DoRA comparison shows that DoRA performance degrades across epochs, while LoRA remains more effective for fine-grained classification. Qualitative evaluation using nine LLM judges yields G-Eval win rates of 0.81--0.96, while five domain experts prefer BananaVLM in 98.48% of 990 blind pairwise comparisons. These results show that lightweight domain adaptation with automated instruction tuning is an effective and scalable approach for specialized agricultural AI. Code, datasets, and model weights are available at https://github.com/samy101/banana-vlm
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