Detection of leaf folder and yellow stemborer moths in the paddy field using deep neural network with search and rescue optimization

In agriculture, insect pests must be identified at the initial stage of infestation to avoid their spread in the field. Leaf folders (cnaphalocrocis medinalis) and yellow stemborers (scirpophaga incertulas) are destructive pests of paddy crops, which are causing severe yield loss. Manual identification of insect pests in the crop is time-consuming, tedious, and ineffective. This paper focuses on a light trap based four-layer deep neural network with search and rescue optimization (DNN-SAR) method to identify leaf folders and yellow stemborers. Light traps are designed to lure the insects in the paddy field and the images of trapped insects are analyzed using the proposed detection method. In the DNN-SAR, images are contrast-enhanced using deer hunting algorithm, impulse noise is removed with fast average group filter, and segmented using social ski-driver optimization. The search and rescue optimization algorithm is used for the selection of optimal weights in the deep neural network, which has improved the convergence rate, lowered the complexity of learning, and improved the accuracy of detection. The proposed method outperformed the existing methods and achieved 98.29% pest detection accuracy.