Citrus fruits diseases and insect pest recognition based on multifractal analysis of Fourier transform spectra

Abstract: Plant pests and diseases image recognition is one of the key technologies of digital agricultural information collection and processing. Usually, based on pest infestation-like plant, it is carried out according to the size, shape, color, texture, etc., or a combination of several parameters. Machine recognition of diseases and insect pests needs to use digitalized characteristics without overlapping. Multi-fractal analysis of Fourier transform spectra was adopted to investigate the possibility of extraction of damage pattern characteristics for Citrus reticulata Blanco var. Ponkan. First, images of the boundary of a damaged pattern are extracted with an improved watershed algorithm and region merging. Secondly, a Discrete Fourier Transform (DFT) was applied to the damaged fruit image. With reference to the boundary of a damaged pattern, a fruit image magnitude spectrum was extracted. Thirdly, a fruit image magnitude spectrum was multi-fractiously analyzed and the multi-fractal spectrum of DFT magnitude spectrum was quadratic fitted. Height, width, and centroid coordinate of a fitting parabolic section were chosen feature values to identify the diseases and insect damage of fruits, with these three feature values as inputs of a BP neural network identifying diseases and insect damage of Ponkan, and the accuracy was up to 92.67%. Finally, the amplitude spectrum of the Fourier transform was adopted for multifractal analysis and multi-fractal spectrum of a quadratic fit; fit parabola segment height, width, and centroid coordinates were regarded as pests' Eigen values, and then used as input variables to establish a BP citrus pest identification neural network model for pest identification. Among 5 classes of pests, in 30 groups of test samples, such as Pezothrips Kellyanus, Oxycetonia Jucunda, Oraesia Emarginata, Polyphagotarsonemus Latus, Colletotrichum Gloeoporioides Penz, the highest recognition rate was for Oraesia Emarginata, that is 96.67%, Polyphagotarsonemus Latus was the lowest at 86.67%, and the average correct recognition rate was 92.67%. The test came to the conclusion that the height, width, and centroid of a multi-fractal spectrum of a Fourier transform spectrum of damaged fruit image better illustrates the features of the disease and insect damage of fruits, such as a complicated biological entity. This method is possibly applicable to automatic recognition of disease and insect damage of Citrus reticulata Blanco var. Ponkan, and it's able to be applied to disease and insect damage recognition for other plants.