This research delves into the innovative application of Explainable Artificial Intelligence (XAI) within wheat grain identification. Employing sophisticated machine learning models augmented by XAI techniques, the study aims to enhance the transparency and comprehensibility of decision-making processes associated with classifying wheat grains. Key objectives include refining model accuracy, imparting insights into critical identification-influencing characteristics, and developing an intuitive user interface tailored for end users, particularly farmers. Through a methodical analysis, the research underscores the significance of XAI in detecting flaws and fine-tuning the model, ultimately bolstering its reliability. The findings of this investigation carry implications for advancing agricultural practices, fostering stakeholder trust, and adapting to the ever-evolving dynamics of the environment.