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Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection.

The automatic detection of leaf disease is necessary to improve the quality and quantity of agricultural production. This paper proposes a framework based on optimal deep neural network (ODNN) to detect the plant leaf disease using the leaf images of healthy and diseased plants. The proposed work uses Convolutional Neural Network for feature extraction and ODNN is used for detection. In ODNN, a two level of weight optimization is employed to boost the performance of the traditional Deep Neural Network. A two level of weight optimization is done by Improved Butterfly Optimization Algorithm. Here the traditional Butterfly Optimization Algorithm is improved using Genetic Algorithm; this double weight updation improves the convergence rate to a considerable amount. Sensitivity, Accuracy and Specificity metrics are used for evaluating the performance of the proposed method. Experimental evaluations were done with the help of MATLAB. The proposed disease detection methodology achieves an overall accuracy of 99 percent. The experimental results give an indication that the proposed method turned out to be a better technique when compared to the existing techniques.

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