Multispectral images of apples for ripeness, sweetness and variety grading [Data set]

This dataset originates from an extensive research effort focused on the development and application of a cost-effective, custom-built multi-spectral imaging chamber designed for evaluating the quality of apples. The imaging setup is capable of capturing spectral reflectance across a range of wavelength bands, enabling detailed, non-invasive analysis of fruit characteristics such as ripeness, sweetness, and varietal differences. In this study, a total of 8 wavelengths were considered. All images were collected under uniform and controlled lighting to minimize environmental variability and enhance data consistency.

For Grading by Ripeness, three classes were considered: Under-Ripe, Ripe and Over-Ripe. For Grading by Ripeness, the following five types of apples were considered, each of four in quantity.
1. Red Delicious (USA)
2. Royal Gala
3. Red Delicious (New Zealand)
4. Washington
5. Kinnaur

For Grading by Variety, three classes- Red Delicious USA, Alpita and Royal Gala were considered. For Grading by Variety for all 3 types, each of the seven in quantity was taken.

For Grading by Sweetness, four classes were considered according to sugar content in % Brix in apples: 10, 12, 13, and 15 classes. For Grading by Sweetness, five varieties were considered, each of four in quantity. The following types of apples were considered for Grading by Sweetness.
1. Red Delicious (USA)
2. Royal Gala
3. Red Delicious (New Zealand)
4. Washington
5. Kinnaur
The images are concatenated with the help of MATLAB code and the concatenated dataset is created for grading by sweetness, ripeness and variety. For this study, APPLENET, a CNN-based architecture, was used to process the concatenated images, and the accuracy achieved was 87 %,65 % and 92 % for grading by ripeness, sweetness and variety, respectively.
The dataset is labelled and structured to support a wide range of applications, particularly in the domains of agricultural technology and food quality monitoring. It offers possible use cases for developing classification and regression models for fruit grading, maturity evaluation, and early detection of surface-level defects. Researchers working on agricultural automation, deep learning in food quality inspection, or horticulture may find this dataset particularly valuable.

Beyond apples, the methodology used for image acquisition and data annotation can be adapted for other fruits, offering scalability for broader agricultural research. The detailed documentation and consistent imaging protocol enhance reproducibility, making this dataset a useful benchmark for relative studies.

This data collection contributes meaningfully to ongoing efforts in computer vision and AI-powered agriculture by providing a reliable, annotated source of multi-spectral fruit images for non-destructive quality evaluation.