期刊
MATHEMATICS
卷 10, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/math10040580
关键词
convolutional neural network model; ECNN; deep neural network; cassava leaf disease identification; global average election polling layer
类别
资金
- Nigerian Petroleum Technology Development Fund (PTDF)
- German Academic Exchange Service (DAAD) [57473408]
- Ministry of Science and Technology, Taiwan [MOST 110-2410-H-165-001-MY2, MOST 110-2410-H-030-032]
This study proposes a comprehensive learning strategy for real-time Cassava leaf disease identification based on an enhanced CNN model. The experimental results show that the proposed ECNN model performs well on a balanced dataset, improving classification performance.
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.
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