Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

The identifcation of diseases in plants contributes an important role in captivating disease control methods for the improvement of quality and quantity of crop yield. Mango trees are afected by diferent diseases and the identifcation of diseases is a tedious task till now when those diseases are manually detected. This paper proposes the novel hybrid Coyote Grey Wolf optimization (CO-GWO) algorithm for the classifcation of mango leaves as
normal or diseased. The classifcation process is done through the extraction of signifcant features from the segmented image. The Neural network (NN) classifer performs the classifcation task, with the weights being adjusted using the proposed algorithm that acts a major role in the enhancement of the classifcation accuracy. The efectiveness of the proposed model is evaluated concerning the evaluation metrics, namely accuracy, precision, recall, and F1 measure, and is attained to be 96.7111%, 97.5712%, 97.1504%, and 96.4792%, respectively. This shows the superiority of the proposed technique in the efective classifcation of mango leaf classifcation as compared with the existing techniques