<p>Sustainable growth in agriculture leverages the advancement of civilization and indeed economic growth. Soil detection extents a major role in achieving significant crop production. In the era of technological advancement and diversity, the manual soil type detection activity can be automated and impeccable detection of the soil can be justified by various innovative approaches. This research manifests the soil type detection of the real time images with deep learning (DL) techniques. Various state-of-the-art algorithms are used to make the solution explainable that eradicate the opacity of the model. The widespread use of smartphones, internet even in rural areas, this work has the potential to completely revolutionize the agricultural sector by identifying the proper soil using computer vision techniques and justify the result with explainable AI (XAI). This efficient solution can naturally improve crop selection and yield optimization, resource management with less environmental impact, enhance farmers’ confidence and bring economic growth. This work introduces a new method for real-time soil type identification that juxtaposes deep learning techniques with explainable artificial intelligence (XAI) to provide precise classification and interpretable decision- making. This research opposes the current practice of treating soil classification as a black-box operation, integrates the merits of DenseNet121, Grad-CAM, and LIME to identify the most important soil features like texture, granularity, and color patterns to influence model predictions and enhance user trust. The solution has been accessible via smartphones, enabling farmers to take pictures of soil samples in real time to obtain immediate, explainable results without the need for ancillary devices. The proposed system surpasses other state-of-the-art models, including DNN, InceptionV3, and Visual Transformer for eight different soil types with remarkable insights.</p>