Traditional machine learning met hods, such as support vector machines (SVM), artificial neural networks (ANN), and logistic regression, have been employed to classify dates based on morphological features like color, t exture, and shape. While effective, these approaches often lack the flexibility and comprehensive quality control needed in modern agricultural practices. To address these limitations, the SmartDate system represents a significant technological advancement by integrat ing deep learning with genetic algorithms and reinforcement learning. This AI-driven system not only excels in date fruit classification but also predicts expiration dates, filling a cruci al gap in existing solutions. SmartDate leverages multispectral and hyperspectral imaging, coupled with Visible-Near-Infrared (VisNIR) spectral sensors, to assess key quality indicators such as moisture c ontent, sugar levels, firmness, and internal defects. This allows for a more thorough evaluation of fruit quality compared to co nventional methods. Moreover, the inclusion of reinforcement learning e nables SmartDate to adapt in real-time to production envir onment changes, optimizing sorting accuracy and ensuring t hat only premium quality dates reach the market.

Many countries such as Saudi Arabia, Morocco and Tunisia are among the top exporters and consumers of palm date fruits. Date fruit production plays a major role in the economies of the date fruit exporting countries. Date fruits are susceptible to disease just like any fruit and early detection and intervention can end up saving the produce. However, with the vast farming lands, it is nearly impossible for farmers to observe date trees on a frequent basis for early disease detection. In addition, even with human observation the process is prone to human error and increases the date fruit cost. With the recent advances in computer vision, machine learning, drone technology, and other technologies; an integrated solution can be proposed for the automatic detection of date fruit disease. In this paper, a hybrid features based method with the standard classifiers is proposed based on the extraction of L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features for the early detection and classification of date fruit disease. A dataset was developed for this work consisting of 871 images divided into the following classes; Healthy date, Initial stage of disease, Malnourished date, and Parasite infected. The extracted features were input to common classifiers such as the Random Forest (RF), Multilayer Perceptron (MLP), Na\"ive Bayes (NB), and Fuzzy Decision Trees (FDT). The highest average accuracy was achieved when combining the L*a*b, Statistical, and DWT Features.