Image recognition of navel orange diseases and insect pests based on compensatory fuzzy neural networks

In order to develop a universal machine vision alogorithm to identify disease and pests of naval orange, blue component of images of naval orange with disease and insect pests was processed with background removed to detect and extract the boundary of disease and insect pests symptoms with improved watershed algorithm. With this boundary the disease and insect pests areas of the original color image were marked. Red, green, and blue components in marked area were used to characterize the color features, and boundary fractal dimension of disease and insect pests area was taken as the shape feature. With the four feature values as compensatory fuzzy neural networks (CFNN) inputs, the CFNN mapper was established to identify diseases and insect pests. The test results showed that the average recognition correctness rate was up to 85.51% for four kinds of plant diseases and insect pests and mechanical damage. This method can be used to identify navel oranges plant diseases and insect pests.