Drought Stress Detection in the Middle Growth Stage Of Maize Based On Gabor Filter and Deep Learning

Drought has become a major factor that limits maize production. This research is mainly focused on maize drought detection. The traditional detection method is mainly based on manual measurement. However it’s time consuming and costly, and some related methods may damaged to plants. In recent years, with the breakthrough of computer vision technology, measurement methods based on image processing technology have begun to be widely used. Image processing is not only low cost but also convenient for real-time analysis. According to some research, the water supply of maize in the two weeks before and after the pollination period will determine the final yield [1]. Therefore, the identification of drought in the middle of maize growth (we define the middle growth stage as 12-leaf to silking stage) is important for final yield. On the basis of this situation, an automatic detection system for drought stress in the middle growth stage of maize is proposed. We use different directions and wavelengths of Gabor filter to obtain the texture feature and then constitute a feature matrix after blocking and condensing features. Finally, the data were fed to the convolution neural network for secondary feature extraction and classification. The average recognition rate of the experiment is 98.84%. The final experimental results shows that our model has the adaptability to illumination and angle transformation, and it can also adapt to a complex field environment.