Chakrabarty, AmitabhaShahriar, HasanShuvo, Protick SarkerFahim, Md. Saidul HaqueSordar, Md SobujHaque, Md Esadul2022-05-182022-05-1820222022-01ID 20301476ID 16301078ID 16301053ID 17301148ID 16201032http://hdl.handle.net/10361/16633Cataloged from PDF version of thesis.Includes bibliographical references (pages 32-35).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Cassava is a high-protein and nutrient-dense plant, notably inside the leaves. Cassava is often used as a rice alternative. Pests, viruses, bacteria, and fungus may cause a variety of illnesses on cassava leaves. This study consists of four main diseases that commonly affect cassava leaves: Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD) and we took these four diseases as labels in our research. Furthermore, we took 22000 infected images from Kaggle and we have transformed our dataset into four different image transformation to ensure the accuracy of our model. These four different augmentations are Random Crop Augmentation, Random Flip Augmentation, Random Rotation Augmentation and Random Contrast Augmentation. Finally, we used six algorithms to detect the diseases of cassava leaves. These six algorithms are Xception, EfficientNetB0 Resnet50, VGG16 Densenet121, InceptionV3. While we operated these algorithms on our trained dataset, it gave diverse precision. For the Xception, it gave 91.3% accuracy, EfficientNetB0:91.1%, ResNet50: 85.0 %, VGG16: 68.0 %, DenseNet121: 87.0 % and for the InceptionV3, it gave 86.4 % precision respectively. Here, not every one of the algorithms performed well. Xception and EfficientNetB0 have the most noteworthy accuracy among these.35 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.Deep learningCassava leafPredictionDecision treeXceptionNeural networksEfficientNet B0Resnet 50VGG16Inception V3DenseNet 121Machine learningCognitive learning theory (Deep learning)Neural networks (Computer science)Thesis