arXiv (All)
open access
PlantPlotGAN: A Physics-Informed Generative Adversarial Network for Plant Disease Prediction
Monitoring plantations is crucial for crop management and producing healthy harvests. Unmanned Aerial Vehicles (UAVs) have been used to collect multispectral images that aid in this monitoring. However, given the number of hectares to be monitored and the limitations of flight, plant disease signals
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
- Context-aware quantile baselines for disturbance detection in non-stationary short-rotation forest plantations#1065764
- Clasificación de tipos de vegetación con Inteligencia Artificial en datos satelitales en El Salvador#1336611
- Integrating High-Throughput Plant Phenomics and Artificial Intelligence for Precision Agriculture#426585
- Sentinel-1 SAR time series for forest phenology: multimodal deep learning with meteorological fusion across deciduous broadleaf and evergreen needleleaf forests#1219199
- VNIR Hyperspectral Signatures and Machine Learning for Early Detection and Classification of Barley Diseases.#317722
Record · ID 1188263
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