Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn

被引:0
|
作者
Barman, Shohag [1 ]
Al Farid, Fahmid [2 ]
Raihan, Jaohar [3 ]
Khan, Niaz Ashraf [3 ]
Bin Hafiz, Md. Ferdous [3 ]
Bhattacharya, Aditi [3 ]
Mahmud, Zaeed [3 ]
Ridita, Sadia Afrin [3 ]
Sarker, Md Tanjil [2 ]
Karim, Hezerul Abdul [2 ]
Mansor, Sarina [2 ]
机构
[1] Bangabandhu Sheikh Mujibur Rahman Sci & Technol Un, Dept Comp Sci & Engn, Pirojpur 8500, Bangladesh
[2] Multimedia Univ, Fac Engn, Cyberjaya 63100, Malaysia
[3] Univ Liberal Arts Bangladesh, Dept Comp Sci & Engn, Dhaka 1207, Bangladesh
关键词
support vector machines (SVMs); convolutional neural networks (CNNs); classification; feature extraction; EfficientNetB0;
D O I
10.3390/jimaging10080183
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
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.
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页数:12
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