Date Fruit Classification with Machine Learning and Explainable Artificial Intelligence

Fruit research now has reached a new dimension thanks to machine learning, which produces actionable insights for further exploration by practitioners in the agricultural domain.In order to automatically categorize the edibility of date fruit, we investigated various types of date fruits and used explainable artificial intelligence (XAI) techniques combined with machine learning-based methods to effectively classify and explain the classification task.Our result shows that with a formidable accuracy ranging within the 90-92 percentile for seven methods, including boosting, bagging, support vector machine (SVM), k-nearest neighbor (KNN), and MLPs, the machine learning methods combined with Local Interpretable Model-Agnostic Explanations (LIME) based XAI provides better actionable insights which can be utilized by domain experts and stakeholders to produce and supply quality fruits particularly date fruits thus contributing in broader perspectives with respect to this dynamically evolving domain.The implementation of the investigation and experiment is available in Github.