Alam, Md. AshrafulMahbub, Sheikh AlimaSiddique, MayishaHasan, TasmiaTasmeem, NaziaProma, Rubaba Aziz2024-10-012024-10-01©20242024-06ID 20101517ID 20101395ID 21101325ID 19101151ID 20101606http://hdl.handle.net/10361/24266Cataloged from PDF version of thesis.Includes bibliographical references (pages 34-35).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.One of the major hindrances to sustainable agriculture and an imminent threat to food security is plant disease. Constantly monitoring a plant’s health and spotting the problems in it is quite painstaking because it demands a lot of work, human resources for visualization, and knowledge of plant diseases. However, deep learning can be extremely useful in the early diagnosis of plant disease, which will minimize productivity loss and help to achieve the objective of sustainable agriculture. In this study, we will use image processing of the leaves to detect plant illness using a vision-based automatic method that uses deep learning models for disease classification such as ResNet50,Densenet121, VGG-16, Inception V3 and Vision Transformers. These techniques are plant image based algorithms.44 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.Image processingDeep learningDisease detectionResNet50DenseNet-121Vision transformersMachine learning.Thesis