Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods.

Checking your browser before accessing pmc.ncbi.nlm.nih.gov ... Click here if you are not automatically redirected after 5 seconds.

Related documents

• Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning#179561
• A hybrid deep learning approach for winter wheat yield prediction: evidence from leveraging multi-source data.#923696
• YOLO-ACBG: an enhanced deep learning model for precision monitoring of wheat stripe rust using different vegetation indices.#371444
• An Input-Frugal Deep Learning Framework for Weather-Driven National Crop-Yield Forecasting: A Case Study of Brazilian Soybean#1143048
• SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction.#165748