Citation

Barman, Shohag and Al Farid, Fahmid and Raihan, Jaohar and Khan, Niaz Ashraf and Hafiz, Md. Ferdous and Bhattacharya, Aditi and Mahmud, Zaeed and Ridita, Sadia Afrin and Sarker, Md Tanjil and Abdul Karim, Hezerul and Mansor, Sarina (2024) Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn. Journal of Imaging, 10 (8). p. 183. ISSN 2313-433X

Text Optimized Crop Disease Identification in Bangladesh_ A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Agriculture plays a vital role in Bangladesh’s economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0′ s feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural networks
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:01
Last Modified:	01 Oct 2024 01:01
URII:	http://shdl.mmu.edu.my/id/eprint/12993

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