A novel algorithm for damage recognition on pest-infested oilseed rape leaves

Cabbage caterpillar infestation of oilseed rape will leave wormholes on leaves. The percentage of wormholes' area on leaf is an effective index to evaluate infestation seriousness. Hyperspectral imaging technology can be used to extract leaf from non-vegetation objects efficiently. Wormhole reconstruction can then be carried out for counting the wormholes' area. The reconstruction of wormholes that are entirely within the leaf contour can be easily processed by holes filling function. However, it is difficult to process wormholes at the edge of a leaf. A novel location factor and an improved genetic-wavelet neural network reconstruction algorithm (G-WNNRA) have been proposed in this paper to process wormholes at the edge of a leaf. For the edge of a damaged leaf, the infested part represented by a hole at the edge and non-infested part should be distinguished automatically. Thus the novel location factor which was based on the first derivative of inverse function was used to develop test function for locating the infested part. Then the proposed G-WNNRA was constructed to reconstruct the missing part of an edge following the step of learning the non-infested part of the edge. The topologicalstructure and parameters of the G-WNNRA was optimized by genetic algorithm and morlet wavelet function was applied as a transfer function. The points on non-infested part of edge were adopted as the training data set and the missing part of the edge were predicted. During the prediction, the points making up the reconstructed edge were chosen based on the output of the G-WNNRA. For performance comparison, wavelet neural network (WNN), genetic neural network (GNN) and back propagation neural network (BPNN) were tested on infested oilseed rape leaves and the RMSE of G-WNNRA was smaller than those of WNN, GNN and BPNN. The proposed location algorithm and G-WNNRA can be combined to reconstruct infested oilseed rape leaves.