Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

Three Essays on Decision Making under Uncertainty in Electric Power Systems

This thesis consists of three essays, discussing three different but connected problems on decision making under uncertainty in electricpower systems.The first essay uses a system model to examine how various factors affect the market price of electricity, and decomposes the price toquantitatively evaluate the contributions of individual factors as well as their interactions. Sensitivity analysis results from a parametric quadratic program are applied in the computation.The second essay formulates the well studied security constrained economic dispatch (SCED) problem as a Markov decision process model,where the action space is a polyhedron defined by linear generation and transmission constraints. Such a model enables the decision maker to accurately evaluate the impact of a dispatch decision to the entire future operation of the electric power system.The third essay examines the effect of demand and supply side uncertainties on the exercise of market power. Solutions under Bertrand, Cournot, and linear supply function equilibrium (LSFE)models are derived and compared.The three problems studied in the essays are a unique representation of different levels of the decision making process in a sophisticated deregulated electric power system, using techniques from both mathematical programming and probability/statistics

Three new approaches to genomic selection

Conventional genomic selection approaches use breeding values to evaluate individual plants or animals and to make selection decisions. Multiple variants of breeding values and selection approaches have been proposed, but they suffer two major limitations. First, selection decisions are not responsive to changes in time and resource availability. Second, selection decisions are not coordinated with related decisions such as mating and resource allocation. We present three new genomic selection approaches that attempt to address these two limitations, which were designed by engineering students in a class project at Iowa State University. Compared with previous approaches using the same data set from the literature, two of these engineering approaches were found to be comparable to the state of the art, and the third one significantly dominated all the previous approaches

BSG alignment of SDSS galaxy groups

We study the alignment signal between the distribution of brightest satellite galaxies (BSGs) and the major axis of their host groups using SDSS group catalog constructed by Yang et al. (2007). After correcting for the effect of group ellipticity, a statistically significant (~ 5\sigma) major-axis alignment is detected and the alignment angle is found to be 43.0 \pm 0.4 degrees. More massive and richer groups show stronger BSG alignment. The BSG alignment around blue BCGs is slightly stronger than that around red BCGs. And red BSGs have much stronger major-axis alignment than blue BSGs. Unlike BSGs, other satellites do not show very significant alignment with group major axis. We further explore the BSG alignment in semi-analytic model (SAM) constructed by Guo et al. (2011). We found general good agreement with observations: BSGs in SAM show strong major-axis alignment which depends on group mass and richness in the same way as observations; and none of other satellites exhibit prominent alignment. However, discrepancy also exists in that the SAM shows opposite BSG color dependence, which is most probably induced by the missing of large scale environment ingredient in SAM. The combination of two popular scenarios can explain the detected BSG alignment. The first one: satellites merged into the group preferentially along the surrounding filaments, which is strongly aligned with the major axis of the group. The second one: BSGs enter their host group more recently than other satellites, then will preserve more information about the assembling history and so the major-axis alignment. In SAM, we found positive evidence for the second scenario by the fact that BSGs merged into groups statistically more recently than other satellites. On the other hand, although is opposite in SAM, the BSG color dependence in observation might indicate the first scenario as well.Comment: 8 pages, 11 figures, ApJ accepte

Seismicity Enhances Macrodispersion in Finite Porous and Fractured Domains: A Pore-Scale Perspective

Understanding the effects of oscillating flow field induced by seismicity on the transport process is vital for predicting the fate and transport of solute in many dynamic environments. However, there is prominent discrepancy in arguing with the response of dispersion to the oscillating flow field (i.e., the longitudinal dispersion coefficient would decrease, increase, or maintain unchanged). To unravel the underpinning physics about this controversial response, we simulated two-hundred twenty pore-scale numerical experiments for the seismicity-induced oscillating flow field and associated solute transport in the idealized finite porous (i.e., fluidic plate) and fractured (i.e., parallel plates) domains. The numerically obtained breakthrough curves were fitted to the macroscopic advection-dispersion equation to retrieve the mean velocity and apparent macrodispersion coefficient (DL). We found that DL increases to its maxima when the oscillating flow field resonates with the finite systems, that is, the period (T) of the oscillating flow field or the seismic wave approaches the pore volume (τ) of a finite domain. The resonant effects diminish and DL barely changes when T is much larger or smaller than τ. Moreover, the degree of enhancement in DL increases exponentially with the amplitude of the seismic force. Fundamental understanding of the response of macrodispersion to the oscillating flow field adds value in predicting the fate of solute in transient flow systems via the advection-dispersion equation

A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings

Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach

Experimental corn hybrids are created in plant breeding programs by crossing two parents, so-called inbred and tester, together. Identification of best parent combinations for crossing is challenging since the total number of possible cross combinations of parents is large and it is impractical to test all possible cross combinations due to limited resources of time and budget. In the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical yield performances of around 4% of total cross combinations of 593 inbreds with 496 testers which were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical yield data collected from crossing other inbreds and testers. In this paper, we present a collaborative filtering method which is an ensemble of matrix factorization method and neural networks to solve this problem. Our computational results suggested that the proposed model significantly outperformed other models such as LASSO, random forest (RF), and neural networks. Presented method and results were produced within the 2020 Syngenta Crop Challenge.Comment: 13 pages, 4 figure