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Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.

For a globally recognized planting breeding organization, manually-recorded field observation data is crucial for plant breeding decision making. However, certain phenotypic traits such as plant color, height, kernel counts, etc. can only be collected during a specific time-window of a crop's growth cycle. Due to labor-intensive requirements, only a small subset of possible field observations are recorded each season. To help mitigate this data collection bottleneck in wheat breeding, we propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making. We call our model WheatNet and show that our approach is robust and accurate for a wide range of environmental conditions of the wheat field. WheatNet uses a truncated MobileNetV2 as a lightweight backbone feature extractor which merges feature maps with different scales to counter image scale variations. Then, extracted multi-scale features go to two parallel sub-networks for simultaneous density-based counting and localization tasks. Our proposed method achieves an MAE and RMSE of 3.85 and 5.19 in our wheat head counting task, respectively, while having significantly fewer parameters when compared to other state-of-the-art methods. Our experiments and comparisons with other state-of-the-art methods demonstrate the superiority and effectiveness of our proposed method., Comment: 14 pages, 5 figures

Regularization is a well-established technique in machine learning (ML) to achieve an optimal bias-variance trade-off which in turn reduces model complexity and enhances explainability. To this end, some hyper-parameters must be tuned, enabling the ML model to accurately fit the unseen data as well as the seen data. In this article, the authors argue that the regularization of hyper-parameters and quantification of costs and risks of false alarms are in reality two sides of the same coin, explainability. Incorrect or non-existent estimation of either quantities undermines the measurability of the economic value of using ML, to the extent that might make it practically useless.

Economic factors can have substantial effects on transportation crash trends. This study makes a comprehensive examination of the relationship between the retail gasoline price (including state and federal fuel taxes) and transportation fatal crashes from 2007 to 2016 in the US. Data on motor vehicle, bicycle and pedestrian fatal crashes come from Fatality Analysis Reporting System (FARS) provided by the National Highway Safety Administration (NHTSA) and the gasoline price data is from U.S. Energy Information Administration (EIA). Random effect negative binomial regression models are used to estimate the impact of inflation-adjusted gasoline prices on trends of transportation fatal crashes. Initial results combined with results of previous studies showed that gender and transportation mean type (motorcycle, non-motorcycle, bicycle and pedestrian) play prominent roles in interpreting the final model, so by using random effect negative binomial regression, seven models are developed to evaluate the effects of gasoline price changes on total population, male, female, motorcyclists, non-motorcyclists, bicyclists and pedestrians separately. Our findings suggest that increasing the gasoline prices will not significantly alter the number of total fatal crashes. However, by looking at different vehicle types, it is estimated that one dollar increase in adjusted gasoline price is associated with 24.2% increase in the number of motorcycle fatal crashes, 1.9% decrease in the number of non-motorcycle fatal crashes, and 0.7% decrease in the number of pedestrian fatal crashes. Also, there is no noticeable difference between male and female in response to the gasoline price changes., Comment: 19 pages, 1 figure, 3 tables

We employ the mathematical programming approach in conjunction with the graph theory to study the structure of correspondent banking networks. Optimizing the network requires decisions to be made to onboard, terminate or restrict the bank relationships to optimize the size and overall risk of the network. This study provides theoretical foundation to detect the components, the removal of which does not affect some key properties of the network such as connectivity and diameter. We find that the correspondent banking networks have a feature we call k-accessibility, which helps to drastically reduce the computational burden required for finding the above mentioned components. We prove a number of fundamental theorems related to k-accessible directed graphs, which should be also applicable beyond the particular problem of financial networks. The theoretical findings are verified through the data from a large international bank., Comment: 32 Pages

The considered problem is how to optimally allocate a set of jobs to technicians of different skills such that the number of technicians of each skill does not exceed the number of persons with that skill designation. The key motivation is the quick sensitivity analysis in terms of the workforce size which is quite necessary in many industries in the presence of unexpected work orders. A time-indexed mathematical model is proposed to minimize the total weighted completion time of the jobs. The proposed model is decomposed into a number of single-skill sub-problems so that each one is a combination of a series of nested binary Knapsack problems. A heuristic procedure is proposed to solve the problem. Our experimental results, based on a real-world case study, reveal that the proposed method quickly produces a schedule statistically close to the optimal one while the classical optimal procedure is very time-consuming.