Deep learning method for leaf-density estimation based on wind-excited audio of fruit-tree canopies

It is essential for precision air-assisted ground sprayer to accurately detect the leaf density of fruit trees in agriculture. However, it is difficult to achieve real-time measurement of canopy leaf densities by using conventional sensors (such as LIDAR and camera). In fact, the interaction between wind and canopies of different leaf densities may generate different excited-audios during air-assisted spraying. Therefore, this paper proposes a method for leaf-density detection of fruit-tree canopies based on the variance of wind-excited audios. First, the fused spectrogram (FSP) was constructed by the short-time fast Fourier transform (STFT), consisting of spectral features and the spectrogram of audio signals. Then, the estimation model between the FSP and leaf densities was developed by using deep convolutional neural network (DCNN), which was enhanced by a spectral centroid attention (SCA) based on the distribution function of spectral centroids. Finally, the test results showed that: (1) the developed model achieved a 96.93% accuracy and a 97.96% precision in leaf-density recognition, and (2) compared with unimproved DCNN model, the accuracy and precision of the developed model were increased about 7.85% and 8.47%, respectively, which indicated that this method could achieve the prediction of leaf-density based on wind-excited audios. The study is expected to provide a reference for leaf-density detection of fruit tree canopies and real-time control of precision spray.