Classification of lemon quality using hybrid model based on Stacked AutoEncoder and convolutional neural network

Agricultural product quality assessment is crucial for determining marketability and managing waste. It helps ensure that products meet industry standards and consumer expectations, leading to increased sales and reduced spoilage. It is important for growers, processors, and distributors to have systems in place to ensure the quality of their agricultural products. When the literature is examined, it is seen that artificial intelligence is used for classification processes of many agricultural products. Many studies carried out in this context are based on processing images of agricultural products with various deep learning and machine learning methods and classifying their quality according to these results. In this study, data sets consisting of statistical properties obtained by GLCM, Color Space, and Morphological methods were combined for the first time in this study and used as a single data set. In addition, hybrid classification processes were carried out by applying dimension reduction methods, such as Stacked AutoEncoder, ReliefF, and deep learning methods, such as CNN, SVC, Ridge Classifier, and Subspace Discriminant, to the created data set. When morphological features were given as input to ML algorithms for normal classification, the SAE–CNN hybrid model we proposed in the study achieved a success above the literature with 98.96% accuracy using 32 features. The experimental results demonstrated the effectiveness of the proposed lemon classification system.