Mitchell Neilsen

Field data collection is a key activity in plant breeding, underpinning efforts to develop crop varieties with improved yields, disease resistance, and climate adaptability. Accurately associating data with the correct field plots is critical for selection and genetic improvement but can be challenging, especially in large experiments. We report the integration of external real‐time kinematic global navigation satellite system (GNSS) receivers into the Field Book application, a widely used tool for recording field data. We introduce a new GNSS trait format for capturing geospatial data, along with a GeoNavigation (GeoNav) feature that guides researchers through fields by automatically identifying and navigating to the nearest plot based on their location. These improvements allow researchers to collect and navigate geospatial information while recording trait data, significantly reducing the risk of associating that data with the wrong plot. To demonstrate the utility of this tool, we performed a case study in a mature peach orchard and showed how the GeoNav feature is used to streamline data collection. Results showed improved efficiency and accuracy in navigating between plots, with minimal issues in repeatability between users. The integration of GNSS into Field Book represents a significant step forward in the precision and ease of data collection for plant breeding programs, enhancing both operational efficiency and data quality.

The global average daily minimum temperatures are increasing at a quicker pace than the average daily maximum temperatures, which are predicted to increase in severity impacting global food production. This study focuses on elucidating the physiological and transcriptional response to high night‐time temperature (HNT) stress in 12 US commercial maize (Zea mays) hybrids using unique field‐based infrastructure. Our experimental objectives were to (i) impose an accurate and uniformly distributed post‐flowering HNT stress of +4.0°C until physiological maturity, (ii) quantify the impact of HNT stress on physiological and yield‐related traits, (iii) establish the impact on end‐use quality of maize kernels formed under HNT stress, and (iv) analyze the differential expression of genes involved in grain starch metabolism. Accurate and uniformly distributed HNT stress of 3.8°C higher than the ambient night‐time temperature throughout the grain‐filling period reduced yield (−14%), kernel weight (−8%), and significantly reduced kernel nutrient content, specifically magnesium in the susceptible hybrids. HNT significantly increased the expression of key genes involved in starch metabolism in the tolerant hybrid. Although HNT stress had a negative impact on yield and quality in field grown maize, two hybrids had physiological and transcriptional regulation that favored higher level of resilience which lays the platform for developing climate smart maize hybrids.

The work detailed here is part of an international initiative on the evaluation of dam safety simulation software for internal erosion performance and best practices. The primary experiments involve simulating uncertainty in the failure events of two dams with two different models: DLBreach and WinDAM C. DLBreach is a physically-based dam/levee breach model developed by Wu. WinDAM C is also a physically based dam breach model capable of analyzing both dam overtopping and internal erosion. The dams selected for the analysis include a 1.3 m high dam tested in the lab and a larger 15.56 m high dam, which suffered a failure in the field. The findings from these experiments are extended with a further analysis on variance, sensitivity, and optimization. Finally, a regression model is trained using the results of these simulators as an inquiry into how well such a system can be captured using machine learning techniques. The results of these experiments, together with the results of the other members of the initiative, improve our understanding of the influences that users bring to using these tools.

In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests in a grain storage or food facility in real time is vital to making insect control decisions. The camera captures the image of the insect and passes it to a Jetson Nano for processing. The Jetson Nano runs a trained deep-learning model to detect the presence and species of insects. With three different lighting situations: white LED light, yellow LED light, and no lighting condition, the detection results are displayed on a monitor. Validating using F1 scores and comparing the accuracy based on light sources, the system was tested with a variety of stored grain insect pests and was able to detect and classify adult cigarette beetles and warehouse beetles with acceptable accuracy. The results demonstrate that the system is an effective and affordable automated solution to insect detection. Such an automated insect detection system can help reduce pest control costs and save producers time and energy while safeguarding the quality of stored products.

Plant breeding is fundamentally comprised of three cyclic activities: (a) intermating lines to generate novel allelic combinations, (b) evaluating the newly created lines in diverse environments, and (c) selecting superior individuals to be used as parents in the next breeding cycle. While digital technologies and tools are commonly utilized for the latter two stages, many plant research programs still rely on manual annotation and paper tags to track the crosses that constitute the basis of a plant breeding program. This presence of analog data is a crack in the foundation of a digital breeding ecosystem and a significant occasion for errors to be introduced that will propagate through the entire breeding process. However, implementing digital cross tracking into breeding programs is difficult due to the nonstandardized workflows that different breeders have adopted. Intercross, an open‐source Android app, aims to provide scientists with a robust and simple solution for planning, tracking, and managing the crosses being made each season and aims to serve as the primary tool to digitize crossing data for breeding programs. The simplicity and flexibility of Intercross allows rapid and broad adoption by diverse breeding programs and will solidify the concepts of a digital breeding ecosystem.

Dam embankment breaches caused by overtopping or internal erosion can impact both life and property downstream. It is important to accurately predict the amount of erosion, peak discharge, and the resulting downstream flow. This paper presents a new model based on the material point method to simulate soil and water interaction and predict failure rate parameters. The model assumes that the dam consists of a homogeneous embankment constructed with cohesive soil, and water inflow is defined by a hydrograph using other readily available reach routing software. The model uses continuum mixture theory to describe each phase where each species individually obeys the conservation of mass and momentum. A two-grid material point method is used to discretize the governing equations. The Drucker–Prager plastic flow model, combined with a Hencky strain-based hyperelasticity model, is used to compute soil stress. Water is modeled as a weakly compressible fluid. Analysis of the model demonstrates the efficacy of our approach for existing examples of overtopping dam breach, dam failures, and collisions. Simulation results from our model are compared with a physical-based breach model, WinDAM C. The new model can capture water and soil interaction at a finer granularity than WinDAM C. The new model gradually removes the granular material during the breach process. The impact of material properties on the dam breach process is also analyzed.

Many agronomic traits have been bred into modern wheat varieties, but wheat (Triticum aestivum L.) continues to be vulnerable to heat stress, with high night-time temperature (HNT) stress shown to have large negative impact on yield and quality. Global mean temperature during the day is consistently warming with the minimum night temperature increasing at a much quicker pace. Currently, there is no system or method that allows crop scientists to impose HNT stress at key developmental stages on wheat or crops in general under field conditions, involving diverse genotypes and maintaining a dynamic temperature differential within the tents compared to the outside. Through implementation of a side roll up and a top ventilation system, heaters, and a custom cyber-physical system using a Raspberry Pi, the heat tents were able to consistently maintain an elevated temperature through the night to differentiate heat stress impact on different genotypes. When the tents were placed in their day-time setting they were able to maintain ambient day-time temperature without having to be removed and replaced on the plots. Data averaged from multiple sensors over three consecutive weeks resulted in a consistent but small temperature difference of 0.25 °C within the tents, indicating even distribution of heat. While targeting a temperature differential of 4 °C, the tents were able to maintain an average differential of 3.2 °C consistently throughout the night-time heat stress period, compared to the outside ambient conditions. The impact of HNT stress was confirmed through a statistically significant yield reduction in eleven of the twelve genotypes tested. The average yield under HNT stress was reduced by 20.3% compared to the controls, with the highest reduction being 41.4% and a lowest reduction of 6.9%. Recommendations for fine-tuning the system are provided. This methodology is easily accessible and can be widely utilized due to its flexibility and ease of construction. This system can be modified and improved based on some of the recommendations and has the potential to be used across other crops or plants as it is not reliant on access to any hardwired utilities. The method tested will help the crop community to quantify the impact of HNT stress, identify novel donors that induce tolerance to HNT and help the breeders develop crop varieties that are resilient to changing climate.