  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.
