Leaf Area Estimation of Reconstructed Maize Plants Using a Time-of-Flight Camera Based on Different Scan Directions

Manuel Vázquez-Arellano,Dimitrios Paraforos,Hans Griepentrog,Miguel Garrido-Izard,David Reiser

doi:10.3390/robotics7040063

Manuel Vázquez-Arellano, Dimitrios Paraforos + Show 3 more

Open Access

https://doi.org/10.3390/robotics7040063

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Abstract

The leaf area is an important plant parameter for plant status and crop yield. In this paper, a low-cost time-of-flight camera, the Kinect v2, was mounted on a robotic platform to acquire 3-D data of maize plants in a greenhouse. The robotic platform drove through the maize rows and acquired 3-D images that were later registered and stitched. Three different maize row reconstruction approaches were compared: reconstruct a crop row by merging point clouds generated from both sides of the row in both directions, merging point clouds scanned just from one side, and merging point clouds scanned from opposite directions of the row. The resulted point cloud was subsampled and rasterized, the normals were computed and re-oriented with a Fast Marching algorithm. The Poisson surface reconstruction was applied to the point cloud, and new vertices and faces generated by the algorithm were removed. The results showed that the approach of aligning and merging four point clouds per row and two point clouds scanned from the same side generated very similar average mean absolute percentage error of 8.8% and 7.8%, respectively. The worst error resulted from the two point clouds scanned from both sides in opposite directions with 32.3%.

Highlights

Information such as stem diameter, plant height, leaf angle, leaf area (LA), number of leaves, and biomass are of particular interest for agricultural applications such as precision farming, agricultural robotics, and automatic phenotyping for plant breeding purposes
The results demonstrated that it was possible to estimate the LA based on the reconstructed surface of maize rows by merging point clouds generated from different 3-D perspective views
The difference between generating the point clouds by scanning the crop row from two sides was very apparent in the resulting average MAPE of 7.8%

Summary

Introduction

Information such as stem diameter, plant height, leaf angle, leaf area (LA), number of leaves, and biomass are of particular interest for agricultural applications such as precision farming, agricultural robotics, and automatic phenotyping for plant breeding purposes. LA is one of the most difficult parameters to measure [3] since manual methods are time-consuming and the 2-D image-based ones are not very accurate because of leaf occlusion and color variation due to sunlight [4]. 3-D imaging could be a good method for a fast and more accurate LA measurement, compared to the 2-D approach, since it does not depend on the position of the leaves (of the plant) in space relative to the image acquisition system [6].

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