Hyperspectral Imaging (Vis/NIR) & ROI-Based °Brix Measurements of Multiple Mango Varieties under various Ripening conditions

This dataset brings together a rich time series of hyperspectral scans and sugar content (°Brix) measurements for 397 mango samples across four premier Indian cultivars—Banganapalli, Senthura, Kalapadi, and Jowari. Scanned daily over their ripening cycle, each fruit was imaged with a push-broom hyperspectral camera (OCI-FL series Bay Spec) covering 400–1000 nm, providing high-resolution spectral data ideal for chemometric analysis, machine-learning modeling, and quality-control applications. Key Features Four Distinct Cultivars: Banganapalli (golden-yellow flesh, premium export quality) Senthura (sweet, fibrous texture) Kalapadi (firm, aromatic meat) Jowari (unique local variety with high acidity) Three Ripening Treatments: Ethylene induction (controlled, accelerated ripening) Natural ambient ripening (no external treatment) Straw-based ripening (traditional, biodegradable method) Daily Scans & Time-Series Metadata: Unique Scan ID per acquisition Exact Date & Time stamp for every hyperspectral image Enables temporal tracking of spectral changes Regions of Interest (ROIs): Top, Middle, Bottom, and Full-Fruit ROIs Independent °Brix measurement for each ROI Supports spatially resolved analysis of sugar distribution Spectral Range & Resolution: 400–1000 nm push-broom imaging Hyperspectral “datacubes” for each Scan ID are stored in the dimension as (Band * Height of the HSI * Width of the HSI) Dataset Contents Metadata CSV listing: Sample ID, variety, ripening method Scan ID, date & time, ROI label Corresponding °Brix reading Why This Dataset Matters Fruit-Quality Assessment: Build robust predictive models of sweetness and maturity. Spectral Modeling & Chemometrics: Explore wavelength bands most sensitive to sugar content. Machine-Learning & AI: Train and benchmark algorithms for non-destructive fruit grading. Postharvest Research: Compare ripening kinetics across cultivars and treatments. Precision Agriculture & Industry: Develop inline HSI-based sorting and quality-control systems.