Tree-based ML models on hyperspectral fruit data achieve high ripeness and firmness prediction accuracy with just three visible wavelengths, offering a low-cost alternative to full hyperspectral systems and deep learning.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models
Tree-based ML models on hyperspectral fruit data achieve high ripeness and firmness prediction accuracy with just three visible wavelengths, offering a low-cost alternative to full hyperspectral systems and deep learning.