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arXiv Machine Learning By Naga Ganesh, Chandrashekar M S, Lakshmi Pedapudi, Aakash Singh, Vineet Singh

Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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