# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
###### Abstract  
The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications. interact of things; Raspberry Pi; LiDAR; GNSS; high-throughput plant phenotyping; precision agriculture +
Footnote †: journal: _bioensors_  
structure from motion systems are principally based on photogrammetric triangulation, which often poses heavy computation overload. Different reflectance that is based imaging sensors such as multispectral and hyperspectral systems have been used to estimate the biomass. The fusion of reflectance and structural information has been reported as being useful to estimate the crop biomass [3; 17]. Other sensors, such as ultrasonic devices and most of the light detection and ranging (LiDAR) systems, are all based on the time-of-flight (ToF) principle [14; 18]. ToF range sensors can directly provide the distance of the target object (i.e., the plant canopy) from the detector to measure crop height. Ultrasonic sensors as ranging devices can provide crop height and biomass measurements that are well correlated with the ground truth measurements, if finely calibrated [19; 20]. Such devices detect objects within their operating sound cone and provide an averaged distance, i.e., inherently filtering the variable crop height within a plot [19]. However, selecting a suitable field-of-view (FoV) is necessary to cover sufficient plant canopy area without exceeding the scanning footprint that is outside the canopy cover to avoid detecting non-plant objects [21]. Additionally, the ultrasonic sensors are sensitive to temperature as sound speed changes with temperature [22] and sound waves are susceptible to plant leaf size, angle, and surfaces [23]. Ranging devices that employ pulsed light compared to sound echoes are technologically faster, providing better a sampling frequency, which is essential when distance is being measured from a mobile field vehicle. A relatively new technology, ToF cameras, were developed to capture color and distance as image arrays simultaneously. These ToF cameras have been found to be promising for 3-dimensional (3D) crop height measurements recently [24; 25]. However, such cameras are sensitive to direct sunlight and require shading to provide accurate measurements [25]. Conventional LiDAR sensors are relatively effective in direct sunlight and can capture a high-resolution representation of the 3D canopy structure [26; 27] and the crop biomass and height can be extracted through post-processing. Although LiDAR is widely accepted and a promising sensor for phenotyping crop biomass and height, it is also costly and LiDAR data can be huge and difficult to process [28]. Internet of Things (IoT)-based systems benefit real-time decision-making with a high level of automation. These systems are becoming more affordable and available for various fields, including agricultural plant phenotyping [25; 26; 27].