Leaf‐density estimation for fruit‐tree canopy based on wind‐excited audio

Abstract It is important to obtain real‐time leaf density of fruit‐tree canopies for the precision spray control of plant‐protection robots. However, conventional detection techniques for the characteristics of fruit‐tree canopies cannot acquire the canopy internal information, which may provide an unsatisfactory accuracy of detection of leaf densities. This paper proposes a method for estimating canopy leaf density of fruit trees based on wind‐excited audio. A wind‐exciting implement was used to force fruit‐tree canopy leaves vibrating to produce audio. Then, some correlation analysis methods were used to extract key characteristic parameters of wind‐excited audio that were significantly correlated with leaf density. Finally, based on the data set of wind‐excited audio, a few machine‐learning methods were used to develop leaf‐density estimation models. Test results showed that: (1) there were five key feature parameters of wind‐excited audio that were significantly correlated with leaf density: the short‐time energy, spectral centroid, the frequency average energy, the peak frequency, and the standard deviation of frequency. (2) the estimation model of leaf density developed based on backpropagation neural network for fruit‐tree canopy showed the optimal estimation results, which can achieve the estimation of leaf density of fruit‐tree canopies accurately. The overall correlation coefficient ( R ) of the estimation model was more than 0.84, the root‐mean‐square error was less than 0.73 m 2 m −3 , and the mean absolute error was less than 0.53 m 2 m −3 . This study is expected to provide a technical solution for the leaf‐density detection of fruit‐tree canopies of plant‐protection robots.