Author
Listed:
- Umesh Kumar Lilhore
(Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)
- Agbotiname Lucky Imoize
(Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany)
- Cheng-Chi Lee
(Research and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic Univesity, New Taipei 24205, Taiwan
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan)
- Sarita Simaiya
(Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)
- Subhendu Kumar Pani
(Krupajal Engineering College, Biju Patnaik University of Technology (BPUT), Rourkela 751002, India)
- Nitin Goyal
(Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)
- Arun Kumar
(Panipat Institute of Engineering and Technology, Panipat, Samalkha 132102, India)
- Chun-Ta Li
(Department of Information Management, Tainan University of Technology, 529 Zhongzheng Road, Tainan 710302, Taiwan)
Abstract
Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers and it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance.
Suggested Citation
Umesh Kumar Lilhore & Agbotiname Lucky Imoize & Cheng-Chi Lee & Sarita Simaiya & Subhendu Kumar Pani & Nitin Goyal & Arun Kumar & Chun-Ta Li, 2022.
"Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification,"
Mathematics, MDPI, vol. 10(4), pages 1-19, February.
Handle:
RePEc:gam:jmathe:v:10:y:2022:i:4:p:580-:d:748265
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References listed on IDEAS
- Deepalakshmi P. & Prudhvi Krishna T. & Siri Chandana S. & Lavanya K. & Parvathaneni Naga Srinivasu, 2021.
"Plant Leaf Disease Detection Using CNN Algorithm,"
International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(1), pages 1-21, January.
- Mohamed Loey & Ahmed ElSawy & Mohamed Afify, 2020.
"Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey,"
International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 11(2), pages 41-58, April.
Full references (including those not matched with items on IDEAS)
Citations
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Cited by:
- Yunlong Ding & Di-Rong Chen, 2023.
"Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks,"
Mathematics, MDPI, vol. 11(15), pages 1-13, July.
- Balaji Natesan & Anandakumar Singaravelan & Jia-Lien Hsu & Yi-Hsien Lin & Baiying Lei & Chuan-Ming Liu, 2022.
"Channel–Spatial Segmentation Network for Classifying Leaf Diseases,"
Agriculture, MDPI, vol. 12(11), pages 1-20, November.
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