ABSTRACT Banana farming plays a crucial role in supporting the livelihoods of people in equatorial and tropical regions. Not only does it support local economies, but it also contributes significantly to food security. However, banana crops are frequently threatened by fungal infections, such as Cordana, Pestalotiopsis, and Sigatoka, which largely affect yield and quality. Intelligent computational methods that identify diseases at an early stage and evaluate severity can greatly enhance the timeliness and effectiveness of plant health interventions. In this context, the current study proposes an innovative integrated platform for automatic detection and estimation of the severity of banana diseases. A hybrid sampling technique, SMOTE‐ENN, is applied for the first time to address class disparity of specimens while removing noisy and potentially mislabeled datasets. Four different Convolutional Neural Network (CNN) architectures have been proposed, labeled CNN1 through CNN4, with varying layer depths and hyperparameters to extract distinctive features from diseased leaf images. Following classification, color thresholding has been used, which uses both HSV and Lab color spaces to measure disease‐specific severity; lesion regions are accurately segmented. The empirical evaluation demonstrated that the 5‐layer CNN2 architecture obtained the best classification accuracy of 96.87%, outperforming the other CNN variants in multiple parameters, including sensitivity, specificity, precision, and F 1 score. Furthermore, the time and space complexities of the models have been analyzed and compared to modern baselines to assess computational efficiency. Following established agricultural norms, the severity percentage is then used to assign a severity grade of the disease and suggest suitable fungicides. The proposed CNN‐based integrated framework facilitates timely interventions, reduces excessive pesticide use, and supports sustainable banana cultivation.
Kaur et al. (2025) studied this question.