HortiVQA-PP: Multitask Framework for Pest Segmentation and Visual Question Answering in Horticulture Article Swipe
Zhongxu Li
,
Chunshui Du
,
Shengrong Li
,
Yaqi Jiang
,
Linwan Zhang
,
Chen Ju
,
Feng Yue
,
Min Dong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/horticulturae11091009
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/horticulturae11091009
A multimodal interactive system, HortiVQA-PP, is proposed for horticultural scenarios, with the aim of achieving precise identification of pests and their natural predators, modeling ecological co-occurrence relationships, and providing intelligent question-answering services tailored to agricultural users. The system integrates three core modules: semantic segmentation, pest–predator co-occurrence detection, and knowledge-enhanced visual question answering. A multimodal dataset comprising 30 pest categories and 10 predator categories has been constructed, encompassing annotated images and corresponding question–answer pairs. In the semantic segmentation task, HortiVQA-PP outperformed existing models across all five evaluation metrics, achieving a precision of 89.6%, recall of 85.2%, F1-score of 87.3%, mAP@50 of 82.4%, and IoU of 75.1%, representing an average improvement of approximately 4.1% over the Segment Anything model. For the pest–predator co-occurrence matching task, the model attained a multi-label accuracy of 83.5%, a reduced Hamming Loss of 0.063, and a macro-F1 score of 79.4%, significantly surpassing methods such as ASL and ML-GCN, thereby demonstrating robust structural modeling capability. In the visual question answering task, the incorporation of a horticulture-specific knowledge graph enhanced the model’s reasoning ability. The system achieved 48.7% in BLEU-4, 54.8% in ROUGE-L, 43.3% in METEOR, 36.9% in exact match (EM), and a GPT expert score of 4.5, outperforming mainstream models including BLIP-2, Flamingo, and MiniGPT-4 across all metrics. Experimental results indicate that HortiVQA-PP exhibits strong recognition and interaction capabilities in complex pest scenarios, offering a high-precision, interpretable, and widely applicable artificial intelligence solution for digital horticulture.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/horticulturae11091009
- https://www.mdpi.com/2311-7524/11/9/1009/pdf?version=1756169104
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413668348
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413668348Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/horticulturae11091009Digital Object Identifier
- Title
-
HortiVQA-PP: Multitask Framework for Pest Segmentation and Visual Question Answering in HorticultureWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-25Full publication date if available
- Authors
-
Zhongxu Li, Chunshui Du, Shengrong Li, Yaqi Jiang, Linwan Zhang, Chen Ju, Feng Yue, Min DongList of authors in order
- Landing page
-
https://doi.org/10.3390/horticulturae11091009Publisher landing page
- PDF URL
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https://www.mdpi.com/2311-7524/11/9/1009/pdf?version=1756169104Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2311-7524/11/9/1009/pdf?version=1756169104Direct OA link when available
- Concepts
-
PEST analysis, Question answering, Segmentation, Horticulture, Biology, Botany, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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