Deep Learning Framework for the Automated Identification and Classification of Mango Disease, Pest and Healthy Conditions

Abstract

Background: Mango (Mangifera indica) is a commercially important fruit crop, but its yield and quality are often threatened by diseases and insect pests. Among the most common are fungal diseases such as sooty mould and powdery mildew and insect pests like gall midge. If not detected early, these cause substantial economic losses to farmers. Manual identification is time-consuming and error-prone, creating the need for automated solutions. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown strong potential in plant disease and pest recognition. Methods: In this study, a sequential CNN-based framework was developed for the automated identification and classification of mango leaf images into four classes: Sooty mould, powdery mildew, gall midge and healthy. The architecture consisted of five convolutional layers with max-pooling for feature extraction, followed by fully connected layers for classification. Model performance was assessed using accuracy, precision, recall, F1-score and confusion matrix analysis. Result: The model achieved an overall accuracy of 96.0%, with high weighted average precision, recall and F1-scores, indicating reliable performance despite class imbalance. The confusion matrix confirmed the model’s capability to distinguish between disease, pest and healthy conditions with minimal misclassification.