Abstract
Plant diseases have a devastating impact on the security of food production, and they cause significant reductions in both the quality and quantity of agricultural products. To reduce this threat, various approaches such as machine learning and deep learning are presented. Therefore, automated recognition and detection of plant diseases is highly preferred in the field of agricultural industry. Most preferred systems utilize a greater number of training parameters but the classification accuracy is low. This paper proposes a deep CNN classifier for disease prediction in rice plant which is optimized by the whale optimization. The performance of the proposed whale based deep-CNN is analyzed using measures such as Accuracy, Sensitivity and Specificity. Based on training percentage Accuracy, Sensitivity and Specificity is 95.60%, 9557, 96.58 and 84%, 81.50, 88.73% for the K-Fold analysis. The accuracy is high when the training percentage is increased thus shows the improved performance outcome when compared with the existing conventional methods.
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Reddy, S.S.S., Sowmya, G., Reddy, V.B., Kumar, B.D., Kumar, A. (2022). Deep CNN Based Whale Optimization for Predicting the Rice Plant Disease in Real Time. In: Kumar, A., Fister Jr., I., Gupta, P.K., Debayle, J., Zhang, Z.J., Usman, M. (eds) Artificial Intelligence and Data Science. ICAIDS 2021. Communications in Computer and Information Science, vol 1673. Springer, Cham. https://doi.org/10.1007/978-3-031-21385-4_17
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