Ashraf, Faisal BinReza, Md. TanzimMohsin, Md. Raiyan BinRamisa, Sadia AfrinSaad, MohammadRabbani, Shahreen HusneTamkin, Salwa2022-06-062022-06-0620222022-01ID 18101639ID 18101469ID 14101135ID 18101134ID 18101511http://hdl.handle.net/10361/16914Cataloged from PDF version of thesis.Includes bibliographical references (pages 48-50).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.The fact that insecticidal pests impair significant agricultural productivity has become one of the main challenges in agriculture. There are, nevertheless, several requirements for a high-performing automated system that can detect pest insects from vast amounts of visual data. We employed deep learning approaches to correctly identify insect species from large volumes of data in this study model and explainable AI to decide which part of the photos is used to categorize the insects from the data. We chose to deal with the large-scale IP102 dataset since we worked with a large dataset. There are almost 75,000 pictures in this collection, divided into 102 categories. We ran state-of-the-art tests on the unique IP102 data set to evaluate our proposed solution. We used five different Deep Neural Networks (DNN) models for image classification: VGG19, ResNet50, EfficientNetB5, DenseNet121, InceptionV3, and implemented the LIME-based XAI (Explainable Artificial Intelligence) framework. DenseNet121 performed best across all classes, and it was also employed to detect crop-specific insect species. The classification accuracy for eight specific crops ranged from 46.31% to 95.36%. Moreover, we have compared our prediction performance to that of earlier articles to assess the efficacy of our research.50 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.IP102Insect pestTransfer learningData augmentationClassificationCognitive learning theory (Deep learning)Machine learning.Artificial intelligenceThesis