Light efficacy estimation for fruit trees based on LIDAR point clouds: A case study on pear trees

Abstract

It is important to evaluate the effect of fruit-tree pruning based on canopy-structure characteristics so as to keep appropriate illumination for tree growth. However, current pruning and evaluation mainly rely on manual experience due to lacking a convenient and automatic approach to obtain such features. This paper proposes a method for estimating light efficacy based on layered canopy point-clouds, where leaf-area was selected as the canopy characteristic. LIDAR was utilised to scan pear trees in three dimensions to acquire initial point clouds, and preprocess was conducted to obtain the point-cloud images of different canopy layers. Then, an optimised algorithm based on Freeman Chain was developed to accurately draw the outlines of point-cloud clusters in the images, and pixel proportion was used to calculate the real leaf-area of each layer. Finally, based on Deep Neural Network, an estimation model was established between different layer leaf-areas and their average illumination acquired in tests. The proposed method was applied to estimate the illumination of a random pear tree to evaluate the variation of light intensity. The results showed that: 1) the average relative error of leaf-area calculation of the optimised Freeman Chain method was about 6.74 %, although the running duration was longer than the conventional one, 2) the overall correlation coefficient, R, of the model was more than 0.95, while that of validation and test sets were more than 0.94, and 3) the illumination estimation was accurate and precise with an average relative error of 1.42 % and a standard deviation of relative errors of 1.19. The validation indicated that this method could achieve the prediction of illumination based on different leaf-area characteristics. The study is expected to give a technical solution for the illumination evaluation before and after tree pruning.