A Semi-Supervised Deep Learning Model for Defective lime Classification

For consumers, the predominant fruit selection criterion is visual quality, a factor that classification models emulate when employing images as input data. Most classification paradigms presuppose a balance across classes. In the field of defective fruit detection, databases commonly exhibit a pronounced imbalance between healthy and defective fruit counts. Such disparity can compromise the robustness of classification models or introduce biases stemming from insufficient data. This study introduces a semi-supervised classification framework based on anomaly detection to identify defective lime fruits (Citrus aurantifolia). The framework employs the reconstruction error obtained from an autoencoder neural network and a calculated anomaly probability to locate samples within a two-dimensional space designed for such purpose. Based on the defined parameter ranges, the limes are categorized as either healthy or defective. The proposed classification model underwent training utilizing the publicly accessible Fruits360 database and was tested with a set of 118 new and unlabeled lime images. The classification model attained a precision of 94%, a recall of 0.88, and an F1-score of 0.91 across the test set. These results corroborate that models based on anomaly detection constitute a promising solution to the inherent challenges of unbalanced classification tasks. They offer the advantage of requiring minimal training data and reduced training times while maintaining efficacy, even when the evaluation dataset diverges substantially from the training set. Thus, the proposed model can serve as a decision support tool for farmers, producers, and consumers.