This study presents a novel approach for detecting diseases in cauliflower using advanced deep-learning techniques. Based on the scope of neural networks such as NasNet Mobile and InceptionV3, cauliflower’s distinct leaf diseases were first detected and then classified in this study. To generate the dataset, pictures were taken under various weather conditions. The integration of NasNet Mobile and InceptionV3 not only improved the detection rate but also increased computational efficiency. This strategy will immensely help if employed during practical activities such as farming. The results elicited the use of a deep learning model to improve existing plant disease diagnosis techniques. It applied of LIME (Local Interpretable Model-agnostic Explanations) and Grad-CAM (Gradient-weighted Class Activation Mapping) as explainable artificial intelligence techniques to enhance the understanding of the suggested model’s output and, therefore, foster decision-making transparency. In terms of the suggested model’s performance metrics, we got a precision of 99.80%, a recall of 100% and F1-score of 99.90%. This innovative method, with its high accuracy and speed, holds promise for widespread adoption by farmers to maintain plant health and optimize crop yields.