arXiv AI
A Conservative OCR-Enabled Workflow for R214 Sodium Screening of South African Packaged Foods
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arXiv Computer Vision
Sep 23
BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis
arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...
arXiv Computer Vision
6d ago
Beyond Group Splits: Specimen-Level Cross-Validation and Visual Attribution for Remaining-Shelf-Life Regression in Climacteric Fruit
arXiv:2610.09726v1 Announce Type: new Abstract: Estimating remaining shelf life (RSL) from images could provide affordable decision support for perishable produce, but evaluation protocols can substa...
arXiv Machine Learning
Sep 21
Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis
The paper presents a configurable multi‑stage vision pipeline for Farmer.Chat, a farm advisory service that processes farmer‑submitted crop photos. It splits the task into a quality gate, a crop detector, and a disease/pest detector, offering two routes: a single fine‑tuned vision‑language model and a set of small specialist models. The new pipeline improves crop accuracy to 95.41% and provides a fast MobileNetV3 quality gate, while retaining the ability to request better photos and answer all queries in one call.
arXiv AI
Aug 5
OliveGemma: A 3 Billion Visual Language Model for Recognising the Mediterranean & European Diet
arXiv:2608. 03428v1 Announce Type: cross Abstract: Image based dietary assessment offers a scalable alternative to self reported food diaries, yet fine-grained food recognition remains challenging due to high intra-class variability and visually similar dishes.
arXiv AI
Jul 10
OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.
arXiv AI
5d ago
Improving Image-Based Nutrition Estimation Through Multimodal Food-Item Verification and Recovery
The paper presents a framework that enhances single-image nutrition estimation by using multimodal large language models to inventory visible foods and verify both food identity and the suitability of proposed 2D regions for portion estimation. A whole-image review identifies gaps and triggers a single targeted recovery pass, after which recovered regions are re-verified and reconciled into a final item set. The approach, which requires no task-specific fine-tuning, improves item-level grounding, mass accuracy, energy accuracy, and item-identity precision and recall compared to a retrieval baseline.