Visual-Language Transformer-Based Tomato Leaf Disease Detection for Portable Greenhouse Monitoring Device

Abstract

Abstract Tomato leaf diseases pose a significant threat to global food security, necessitating accurate and efficient detection methods. This paper introduces the Tomato Leaf Disease Visual Language Model (TLDVLM), a novel approach based on the BLIP-2 architecture enhanced with Low-Rank Adaptation (LoRA), for precise classification of 10 distinct tomato leaf diseases. Our methodology integrates a sophisticated image preprocessing pipeline, utilizing GroundingDINO for robust leaf detection and SAM-2 for pixel-level segmentation, ensuring that the model focuses solely on relevant plant tissue. The TLDVLM leverages the powerful multimodal understanding of BLIP-2, with LoRA applied to its Q-Former module, enabling parameter-efficient fine-tuning without compromising performance. Comparative experiments demonstrate that the TLDVLM significantly outperforms baseline models, including CLIP-LoRA and ConvNeXT-tiny, achieving an accuracy of 97.27%, a precision of 0.9587, a recall of 0.9789, and an F1-score of 0.9681. Beyond classification, the finetuned TLDVLM checkpoints are integrated into a practical application for new image inference. This application displays the raw and segmented images, the predicted disease, and offers functionalities to fetch comprehensive information on disease causes and remedies using external APIs (e.g., OpenAI), with an option to download a PDF summary for offline access on a portable device. This research highlights the potential of LoRA-adapted Vision-Language Models in developing highly accurate, efficient, and user-friendly agricultural diagnostic tools.

Plant phenotyping relevance

トマト葉の病害状態を画像から分類する手法を開発し、複数モデルとの比較実験で性能を検証しているため、植物病害フェノタイピング手法が中心である。

Code and data availability

The paper describes a tomato leaf disease detection pipeline (BLIP-2 LoRA, GroundingDINO, SAM-2) using PlantVillage Kaggle data and self-captured Silal Greenhouse images, but no blocks contain author-deposited datasets, code, model checkpoints, or any public availability statement with a URL. allowed_urls is empty, so

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