Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification

Umesh Kumar Lilhore, Agbotiname Lucky Imoize, Cheng-Chi Lee, Sarita Simaiya, Subhendu Kumar Pani, Nitin Goyal, Arun Kumar and Chun-Ta Li
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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
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
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

Mathematics, 2022, vol. 10, issue 4, 1-19

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.

Keywords: convolutional neural network model; ECNN; deep neural network; cassava leaf disease identification; global average election polling layer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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