arXiv:2606. 28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation.
arXiv:2606. 29243v2 Announce Type: replace Abstract: We introduce KrishokChat, an 85,979-instance Bengali agricultural benchmark built from 284 government publications, 13 institutions, and six regional dialects.
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:2606. 29243v1 Announce Type: new Abstract: We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings.
arXiv:2609.15427v1 Announce Type: cross Abstract: Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions requir...
arXiv:2610.28211v1 Announce Type: cross Abstract: Applying South Africa's draft R3337 warning criteria to food-package photographs requires nutrition, ingredient and product-form evidence. This study...
Applying South Africa's draft R3337 warning criteria to food-package photographs requires nutrition, ingredient and product-form evidence. This study presents a region-guided evidence extraction and a...
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...
FedXCrop is a federated learning pipeline for crop disease classification that preserves farmer privacy while providing post‑aggregation explainability. The system uses a MobileNetV2 model and achieves near‑centralized accuracy (98.81%–99.38%) across five clients, even under non‑IID label skew. Grad‑CAM and SmoothGrad explanations are quantitatively evaluated, showing that federated models largely retain explainability characteristics of centralized models, though agreement decreases with increased heterogeneity.
arXiv:2608.20663v1 Announce Type: new Abstract: Public datasets for agricultural disease detection are usually judged fit for use from reported metrics, which say nothing about whether the annotation...
arXiv:2607. 19262v1 Announce Type: new Abstract: As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis.
arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.