Abstract
Challenges in rapid prototyping are a major bottleneck for plant breeders trying to develop the needed cultivars to feed a growing world population. Remote sensing techniques, particularly LiDAR, have proven useful in the quick phenotyping of many characteristics across a number of popular crops. However, these techniques have not been demonstrated with cassava, a crop of global importance as both a source of starch as well as animal fodder. In this study, we demonstrate the applicability of using terrestrial LiDAR for the determination of cassava biomass through binned height estimations, total aboveground biomass and total leaf biomass. We also tested using single LiDAR scans versus multiple registered scans for estimation, all within a field setting. Our results show that while the binned height does not appear to be an effective method of aboveground phenotyping, terrestrial laser scanners can be a reliable tool in acquiring surface biomass data in cassava. Additionally, we found that using single scans versus multiple scans provides similarly accurate correlations in most cases, which will allow for the 3D phenotyping method to be conducted even more rapidly than expected.
Highlights
Cassava (Manihot esculenta) is a South American crop that was first cultivated between4000 and 2000 B.C
It is estimated that over 278 million tons of cassava [2] are cultivated annually, with its yield primarily serving as a source of carbohydrates for humans and secondarily as animal feed [3]
While cassava leaves are not the most widely consumed part of the plant, they serve as an important vegetable in the Congo [13], as well as an important animal feed for cattle and sheep, due to their high crude protein (~25%) content [14]
Summary
Cassava (Manihot esculenta) is a South American crop that was first cultivated between. Terrestrial laser scanners (TLS) have the ability to record fine details of an object in 3D and often capture additional data such as intensity or RGB images. This is accomplished by emitting pulses of laser light (often at a single wavelength) at an object and using the time of flight and speed of light to determine the distance [18]. Perhaps the most significant finding was that single, unregistered scans have comparable results to those of registered point clouds, drastically reducing the time needed to process data for this type of phenotyping
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