Implementation of DenseNet Architecture With Transfer Learning to Classify Mango Leaf Diseases - Likorawung, Marsha Alexis; Wonohadidjojo, Daniel Martomanggolo (Intelligent System and Computation, 2024)
Mango plants (Mangifera indica) are a significant export commodity in the horticultural industry, offering numerous nutritional and economic benefits. They are rich in essential micronutrients, vitamins, and phytochemicals, contributing to their high demand globally. However, mango plants are susceptible to various diseases that can severely impact their yield and quality. These diseases pose a challenge to mango farmers, many of whom struggle to identify and treat them effectively, leading to potential harvest failures. This study aims to address this challenge by implementing a Deep Learning approach to classify diseases in mango leaves. Specifically, the research utilizes a Convolutional Neural Network (CNN) with DenseNet architecture, known for its efficiency in image classification tasks. The study incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing to enhance detail and improve the model’s performance. Transfer Learning is utilized to optimize the DenseNet model, leveraging a pre-trained model to achieve high accuracy even with a relatively small dataset. The dataset used in this research comprises 4000 labeled images of mango leaves, covering seven disease categories and healthy leaves. These images include common diseases such as Anthracnose, Dieback, Powdery Mildew, Red Rust, Cutting Weevil, Bacterial Canker, and Sooty Mould. The DenseNet model achieved an overall accuracy of 99.5% in classifying mango leaf diseases.