Automated diagnosis of Grape Black Measles (GBM) disease has become a pivotal aspect of modern agribusiness due to its efficiency and rapidity. Manual segmentation and diagnosis of GBM are intricate tasks due to time and cost constraints. In this study, we introduce Grape Leaf Doctor, a novel method for the automatic detection and severity analysis of GBM, utilizing the response surface approach and interval type 2 fuzzy logic inference (IT2FL). Firstly, we employ the DeepLabV3+ semantic segmentation model based on ResNet50 to perform pixel-level predictions on images of grape leaves affected by fungal lesions. This model enables the identification of “regions of interest” (ROIs) and the calculation of the percentage of infections (POI). Subsequently, the IT2FL rule-based system is constructed to assess the severity of disease damage based on these features. In the IT2FL system, Gaussian and trapezoidal “membership functions” (MFs) are explored for inputs and outputs to facilitate fuzzy inference and defuzzification. The severity of GBM infection is categorized into four levels: ‘Healthy’, ‘Mild’, ‘Medium’, and ‘Severe’. The experimental results on the IT2FL hold-out test dataset show a general classification accuracy of 98.34%, whereas RSM achieves 90.69%. By merging image processing and statistical modeling, the DeepLabV3+ framework of the IT2FL system can efficiently recognize GBM across varying disease risks.