Crop improvement programs require large and meticulous selection processes that effectively and accurately collect and analyze data to generate quality plant products as efficiently as possible, develop superior cropping and/or crop improvement methods. Typically, data collection for such testing is performed by field teams using hand-held instruments or manually-controlled devices. Although steps are taken to reduce error, the data collected in such manner can be unreliable due to human error and fatigue, which reduces the ability to make accurate selection decisions. Monsanto engineering teams have developed a high-clearance mobile platform (Rover) as a step towards high throughput and high accuracy phenotyping at an industrial scale. The rovers are equipped with GPS navigation, multiple cameras and sensors and on-board computers to acquire data and compute plant vigor metrics per plot. The supporting IT systems enable automatic path planning, plot identification, image and point cloud data QA/QC and near real-time analysis where results are streamed to enterprise databases for additional statistical analysis and product advancement decisions. Since the rover program was launched in North America in 2013, the number of research plots we can analyze in a growing season has expanded dramatically. This work describes some of the successes and challenges in scaling up of the rover platform for automated phenotyping to enable science at scale.
High throughput (HT) phenotyping of crops is essential to increase yield in environments deteriorated by climate
change. The controlled environment of a greenhouse offers an ideal platform to study the genotype to phenotype
linkages for crop screening. Advanced imaging technologies are used to study plants’ responses to resource limitations
such as water and nutrient deficiency. Advanced imaging technologies coupled with automation make HT phenotyping
in the greenhouse not only feasible, but practical.
Monsanto has a state of the art automated greenhouse (AGH) facility. Handling of the soil, pots water and
nutrients are all completely automated. Images of the plants are acquired by multiple hyperspectral and broadband
cameras. The hyperspectral cameras cover wavelengths from visible light through short wave infra-red (SWIR). Inhouse
developed software analyzes the images to measure plant morphological and biochemical properties. We measure
phenotypic metrics like plant area, height, and width as well as biomass. Hyperspectral imaging allows us to measure
biochemcical metrics such as chlorophyll, anthocyanin, and foliar water content.
The last 4 years of AGH operations on crops like corn, soybean, and cotton have demonstrated successful
application of imaging and analysis technologies for high throughput plant phenotyping. Using HT phenotyping,
scientists have been showing strong correlations to environmental conditions, such as water and nutrient deficits, as well
as the ability to tease apart distinct differences in the genetic backgrounds of crops.
The problem of image formation for X-ray transmission tomography is formulated as a statistical inverse problem. The maximum likelihood estimate of the attenuation function is sought. Using convex optimization methods, maximizing the loglikelihood functional is equivalent to a double minimization of I-divergence, one of the minimizations being over the attenuation function. Restricting the minimization over the attenuation function to a coarse grid component forms the basis for a multigrid algorithm that is guaranteed to monotonically decrease the I-divergence at every iteration on every scale.
We propose an alternating minimization (AM) image estimation algorithm for iteratively reconstructing transmission tomography images. The algorithm is based on a model that accounts for much of the underlying physics, including Poisson noise in the measured data, beam hardening of polyenergetic radiation, energy dependence of the attenuation coefficients and scatter. It is well-known that these nonlinear phenomena can cause severe artifacts throughout the image when high-density objects are present in soft tissue, especially when using the conventional technique of filtered back projection (FBP). If we assume no prior knowledge of the high-density object(s), our proposed algorithm yields much improved images in comparison to FBP, but retains significant streaking between the high-density regions. When we incorporate the knowledge of the attenuation and pose parameters of the high-density objects into the algorithm, our simulations yield images with greatly reduced artifacts. To accomplish this, we adapted the algorithm to perform a search at each iteration (or after every n iterations) to find the optimal pose of the object before updating the image. The final iteration returns pose values within 0.1 millimeters and 0.01 degrees of the actual location of the high-density structures.