Abstract:
Mango is among the most valuable and popular fruits in Bangladesh and most other
tropical nations in commercial terms. Mango farmers, however, experience serious
losses in crop year after year because of numerous fruit diseases and in particular,
Anthracnose, Mango Scab, and Stem-End Rot. To minimize the damage and enhance
the quality of fruits, these diseases should be detected early and correctly.
Conventional detection systems are based on human observation by specialists and
are not always time-consuming and not always accessible to all farmers. As artificial
intelligence in the agricultural sector evolves, machine learning (ML) and deep
learning (DL) methods are more and more applied to identify diseases through the use
of fruit images. In this thesis, we set our target on creating an automated disease
classification system of mango fruit by utilization of image analysis. It will aim at
comparing the performance of five well-known models namely Convolutional Neural
Network (CNN), VGG16, InceptionV3, K-Nearest Neighbors (KNN), and Random
Forest. There was a formation of a dataset of four classes; Anthracnose, Mango Scab,
Stem-End Rot, and Healthy mango; based on both field images and open-source
agricultural data. Resizing, normalization and augmentation were the preprocessing
functions that were used to enhance the quality of the dataset. Standard performance
measures, which included accuracy, precision, recall, and F1-score, were used to train
and evaluate each model. The ultimate result of this research is to determine what
model is the most effective in detecting mango disease using fruit images. The study
helps in the creation of intelligent farming implements that would assist farmers to
make timely decision, minimize production losses and enhance sustainable farming in
Bangladesh and other countries.
