Dates Fruit Disease Recognition using Machine Learning

TL;DR

This paper presents a machine learning-based approach for early detection and classification of date fruit diseases using hybrid features extracted from images, aiming to assist farmers in timely intervention.

Contribution

It introduces a hybrid feature extraction method combined with standard classifiers for accurate disease detection in date fruits, supported by a new dataset of 871 images.

Findings

Highest accuracy achieved with combined L*a*b, Statistical, and DWT features.

Effective classification of healthy, initial disease, malnourished, and parasite-infected dates.

Demonstrates potential for automated disease monitoring in date agriculture.

Abstract

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.