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.
Highlights
Cassava is the main crop in Africa and many other nations
This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN)
This research provides a comprehensive learning method for real-time Cassava leaf disease detection based on an enhanced CNN model (ECNN)
Summary
Cassava is the main crop in Africa and many other nations. Africa is the largest producer of Cassava crops. Cassava can be cultivated successfully in any climate, including drought and unproductive soil. Cassava crops encounter several challenges during production, i.e., leaf diseases and poor quality. Cassava leaf diseases are the principal cause of production reduction, and they can directly affect farmers’ revenue [1]. Cassava leaf disease identification must be treated on a priority basis to improve production capacity. The automatic detection of crop diseases focused on crop leaves is critical in crop production. Effective and accurate detection of leaf diseases significantly affects crop productivity improvement. Cassava leaf diseases are similar to Maize leaf diseases [2]
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