Maps of the spatial distribution of crop heights can strongly support agriculture in terms of efficiency and yield optimization. Recently published results of experiments of the 3D Spatial Data Processing research group describe an approach to easily extend regular agricultural machines with low-cost sensors for capturing crop heights while the machine is in the field.
Based upon a comparison between crop height values derived from 3D geodata captured with the low-cost approach, and high-end terrestrial laser scanning reference data, minimum RMS and standard deviation values of 0.13 m (6.91% of average crop height), and maximum R² values of 0.79 were achieved. A main conclusion of the study is that the crop height measurements derived from data captured with the introduced setup can provide valuable input for tasks such as biomass estimation.
Get more details of the study in:
Hämmerle, M. & Höfle, B. (2017): Mobile low-cost 3D camera maize crop height measurements under field conditions. Precision Agriculture, pp. 1-16. https://doi.org/10.1007/s11119-017-9544-3 – read the PDF online via Springer Nature SharedIt: http://rdcu.be/w24P
Check also other work about agricultural crop height conducted by the 3D Spatial Data Processing research group:
Hämmerle, M., Höfle, B. (2016): Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements. Plant Methods 2016(12:50). http://dx.doi.org/10.1186/s13007-016-0150-6
Marx, S., Hämmerle, M., Klonner, C. & Höfle, B. (2016): 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data. PLOS ONE. Vol. 11 (4), pp. 1-22. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152839
Crommelinck, S. & Höfle, B. (2016): Simulating an Autonomously Operating Low-Cost Static Terrestrial LiDAR for Multitemporal Maize Crop Height Measurements. Remote Sensing. Vol. 8 (3), pp. 1-17. http://dx.doi.org/10.3390/rs8030205
Hämmerle, M., Höfle, B. (2014): Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture. Sensors, Vol. 14, pp. 24212-24230. http://www.dx.doi.org/10.3390/s141224212