Dataset of apples for grading by sweetness, ripeness and variety

The study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system. The system was optimized to allow spectral information to be obtained in 8 discrete wavebands, which enabled non-destructive determination of such key fruit components as ripeness, sugar type and cultivar. Stringent environmental conditions were maintained during image acquisition for optimal measurement consistency and experimental repeatability. The detailed dataset encompasses 32,463 multi-spectral images across three distinct classification categories. For sweetness evaluation, 1620 images spanning Brix values from 10 % to 15 % were collected from five apple varieties. Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. Each image was captured under controlled lighting conditions using eight specific wavelengths, ensuring spectral consistency crucial for machine learning applications. These multi-spectral images were concatenated for grading by sweetness, ripeness, and variety, creating a processed dataset of concatenated images optimized for AppleNet processing. The concatenation process combines the eight wavelength channels into unified image representations suitable for deep learning applications. Sample collection included the picking of different apple cultivars at different physiological development phases of fruit from local orchards. Single specimens were imaged sequentially using a multi-spectral technique. Information on sugar content concentration ( % Brix), maturation phase classification and varietal identification was recorded according to standard laboratory procedure. The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems. The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. Furthermore, the proposed multi-spectral imaging can be generalized to cover other fruits and agricultural products, extending the application of the method in smart agriculture. This dataset serves as a valuable resource for researchers in computer vision, machine learning, and agricultural technology, fostering advancements in non-destructive fruit quality evaluation methodologies.