Rasel, Annajiat AlimChakrabarty, AmitabhaAzmain, Md. AquibHussain, BasitMuradi, MalikaBoateng, ChristianNandi, Eliya ChristopherTresor, Imenagitero Ulysse2025-02-052025-02-05©20242024-10ID 21141064ID 21241057ID 22101816ID 21341039ID 21101333http://hdl.handle.net/10361/25320Cataloged from PDF version of thesis.Includes bibliographical references (pages 71-76).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.Maize is a vital crop that feeds over a billion people worldwide and supports numerous industries. However, maize production is threatened by devastating plant diseases such as Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV), which can lead to significant yield losses and economic impacts, particularly in Sub-Saharan Africa. Therefore, to prevent significant losses of this essential crop, farmers need to be equipped with advanced tool that enables accurate and timely disease detection. In this regards, we have implemented a comparative performance analysis of five Transfer Learning (TF) (EfficientNetV2B2, ResNet50, InceptionV3, VGG16, and Xception) and five Vision Transformer (ViT) (SWIN, DaViT, MobileViT, MaxViT, and Involutional Neural Network (INN)) models for maize crop disease detection. We subsequently developed a fusion model that integrates MobileViT and DaViT. Afterward, the performance of the models was evaluated using multiple metrics such as precision, recall, and f1-score. The proposed fusion model perform best across all the metrics with an accuracy of 96.67%, recall of 95.84%, precision of 96.34%, and a f1-score of 96.54%. For transparent decision-making, three explainable artificial intelligence (XAI) techniques such as saliency map, gradient weighted class activation mapping (Grad-CAM), and local interpretable model agnostic explanations (LIME) have been implemented. Finally, we deployed the proposed fusion model on a Raspberry Pi to facilitate real-time detection of maize diseases.88 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Maize diseasePlant diseaseDisease detectionGradCAMMaize streak virusMaize lethal necrosisXAIExplainable AIVision transformersDeep learning (Machine learning).Artificial intelligence--Agricultural applications.Corn--Diseases and pests--Detection--Agricultural innovations.Thesis