Matthew E. Carroll
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View article: Time Series GWAS for Iron Deficiency Chlorosis Tolerance in Soybean using Aerial Imagery
Time Series GWAS for Iron Deficiency Chlorosis Tolerance in Soybean using Aerial Imagery Open
The use of drones has become a commonly used tool by plant scientists to aid in plant phenotyping endeavors. Iron deficiency chlorosis (IDC) is a commonly observed abiotic stress in soybean fields with high soil pH levels. IDC severity is …
View article: Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference
Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference Open
Introduction Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persi…
View article: Leveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trials
Leveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trials Open
Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P‐Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity.…
View article: AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning Open
Agricultural production requires careful management of inputs such as fungicides, insecticides, and herbicides to ensure a successful crop that is high-yielding, profitable, and of superior seed quality. Current state-of-the-art field crop…
View article: Smart connected farms and networked farmers to improve crop production, sustainability and profitability
Smart connected farms and networked farmers to improve crop production, sustainability and profitability Open
To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in informat…
View article: A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data
A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data Open
Soybean yield prediction is a challenging problem in plant breeding that is often affected by many different factors simultaneously. Hyperspectral reflectance data from plants and soil data provide breeders with useful information about so…
View article: Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials
Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials Open
Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving mean and P-Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Withi…
View article: Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production
Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production Open
To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. There are rapid advances i…
View article: A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data
A Comprehensive Study on Soybean Yield Prediction Using Soil and Hyperspectral Reflectance Data Open
Soybean yield prediction is a challenging problem in plant breeding that is often affected by many different factors simultaneously. Hyperspectral reflectance data from plants and soil data provide breeders with useful information about so…
View article: Self‐supervised learning improves classification of agriculturally important insect pests in plants
Self‐supervised learning improves classification of agriculturally important insect pests in plants Open
Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)‐based approaches for automating agricultural pest detecti…
View article: Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding
Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding Open
High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportuni…
View article: “Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies
“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies Open
Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surfac…
View article: Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications
Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications Open
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean (Glycine…
View article: Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Open
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean [\textit…
View article: Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding\n Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in\n Breeding Applications
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding\n Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in\n Breeding Applications Open
Reliable seed yield estimation is an indispensable step in plant breeding\nprograms geared towards cultivar development in major row crops. The objective\nof this study is to develop a machine learning (ML) approach adept at soybean\n[\\te…
View article: Usability Analysis in Practice: Assessment for Redesign of the School of Information & Library Science Web Site
Usability Analysis in Practice: Assessment for Redesign of the School of Information & Library Science Web Site Open
The University of North Carolina at Chapel Hill's School of Information and Library Science (SILS) web site has existed in its current form for almost three years while web design has evolved from more simple design practices of the late 1…