Limitations to High Throughput Phenotyping

HTP offers many advantages to the plant breeding industry such as reducing the time required for some data collection like plant height. It is also important to discuss some limitations of the technology. A few considerations are:

• Data storage – HTP can collect immense amounts of data, so you need the capacity to store all that data. To give you an idea, saving multispectral images collected by a UAS flown at an altitude of 25 meters over a field that is ~6 acres will require about 15 gigabytes of storage. This is just one example, but as a general rule the size of the field, altitude of flight, overlap, and photo intervals are all going to affect the storage space needed. You will also need to have a proper amount of storage for the processed data.
• Processing Time – This technology increases the volume of data while decreasing the amount of time required for manual data collection. However, there is additional time needed to process the data after collection. For example, to manually measure plant height, LAI, and destructive biomass in 30 research plots will take a team of 5 individuals about 4 hours. To capture this data with a UAS will take one person about 30-40 minutes. The images collected by the UAS will then take roughly 5-6 hours to process.
• Price – Depending on the drone and sensors used, these UAS can come with a big price tag. The DJI Phantom 4 (Fig. 10) is purchased with the sensor included and is on the cheaper end when it comes to UAS. In comparison, the DJI Matrice 300 (Fig. 10) without the sensors included costs approximately six times as much as the DJI Phantom 4. In addition, purchasing the most commonly used RGB and multispectral sensors to attach to the DJI Matrice 300 UAV will double the price. There are also other costs associated with accessories like batteries and purchasing software to process the data.
• Software – This technology is still being developed, which presents a few issues. During drone setup the software can glitch causing startup to take longer than just ‘pushing a button and turning it on’. This can be frustrating for researchers who have a limited window to collect data due to the weather and plant growth stages.
• Indirect measurements – We cannot directly measure every trait of interest, so we must use indirect measurements of traits that correlate with those we are interested in. For example, we cannot distinguish diseases in crops using UAS. Instead, we can use sensors to determine plant health based on the calculated NDVI. Plant health can be correlated with the amount of disease present in a crop. However, plant health can be affected by many factors like drought conditions and nutrient deficiencies, so it is important to still be present in the field to make visual observations.

Despite these concerns, researchers believe UAS will have a beneficial impact in the agriculture community. Scientists are continuing to conduct research to minimize these issues and improve the technology.