A Comparative Analysis of CNN and Transfer Learning Approaches for Mango Leaf Disease Detection

Abstract

The comparative analysis of CNN and transfer learning approaches delves into the application of agriculture, with the specific focus on mango leaf disease detection. The core objective of the study is to develop an effective method for early, accurate and sustainable leaf disease detection system. With the help of convolutional neural networks and transfer learning techniques, this study aims to analyze raw mango leaf images to identify the certain disease. Raw images were collected from local mango orchards that undergo processing and data augmentation to refine model training. The study employs an array of deep learning models, including bespoke CNN models and pre-trained models such as EfficientNetB4 and InceptionV3. These models are conscientiously trained and tested against the dataset, with their performances evaluated on metrics like accuracy, precision, recall, and F1-score. It was observed that, the CNN-02 model, designed with batch normalization and dropout layers, exhibited superior performance in terms of accuracy and generalizability compared to other models, achieving 95.65% of accuracy. Additionally, the EfficientNetB4 model demonstrated an impressive learning capacity, with a precision rate of 98.3%. These results substantiate the effectiveness of both CNN and transfer learning approaches in the realm of leaf disease detection, with certain models showing exceptional accuracy and efficiency and promising potential of CNN and transfer learning techniques in early mango leaf disease detection, making a significant contribution to agricultural technology.