NCBI PubMed Central
open access
MobileNet-WDD: a lightweight deep learning image classification method for identifying defects in wheat grains.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- MT4AFE: A Deep Learning Benchmark Dataset for Agricultural Field Extraction#291828
- Investigating performance and key factors for real-world deployment of grain image classification using convolutional neural networks.#14905
- Rapid rice seed vigor assessment: A machine learning and deep learning framework with multi-time-point image analysis.#382449
- EDISP: a hybrid CNN-ViT framework for robust maize leaf disease detection and classification.#402591
- A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches#479900
Record · ID 371443
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive