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Full-Text Articles in Horticulture
Predicting Site‑Specific Economic Optimal Nitrogen Rate Using Machine Learning Methods And On‑Farm Precision Experimentation, Alfonso De Lara, Taro Mieno, Joe D. Luck, Laila A. Puntel
Predicting Site‑Specific Economic Optimal Nitrogen Rate Using Machine Learning Methods And On‑Farm Precision Experimentation, Alfonso De Lara, Taro Mieno, Joe D. Luck, Laila A. Puntel
Department of Agronomy and Horticulture: Faculty Publications
Applying at the economic optimal nitrogen rate (EONR) has the potential to increase nitrogen (N) fertilization efficiency and profits while reducing negative environmental impacts. On-farm precision experimentation (OFPE) provides the opportunity to collect large amounts of data to estimate the EONR. Machine learning (ML) methods such as generalized additive models (GAM) and random forest (RF) are promising methods for estimating yields and EONR. Twenty OFPE N trials in wheat and barley were conducted and analyzed with soil, terrain and remote-sensed variables to address the following objectives: (1) to quantify the spatial variability of winter crops yield and the yield response …
Non-Destructive Classification And Quality Evaluation Of Proso Millet Cultivars Using Nir Hyperspectral Imaging With Machine Learning, Laruen E. Doyle, Julia R. Loeb, Nader Ekramirad, Dipak K. Santra, Akinbode A. Adedeji
Non-Destructive Classification And Quality Evaluation Of Proso Millet Cultivars Using Nir Hyperspectral Imaging With Machine Learning, Laruen E. Doyle, Julia R. Loeb, Nader Ekramirad, Dipak K. Santra, Akinbode A. Adedeji
Department of Agronomy and Horticulture: Faculty Publications
Millet is a small-seeded cereal crop with big potential and remarkable characteristics such as high drought resistance, short growing time, low water footprint, and the ability to grow in acidic soil. There is a need to develop nondestructive methods for differentiation and evaluation of the quality attributes of different of proso millet cultivars grown in the U.S. Current methods of cultivar classification are either subjective or destructive, time consuming, not allowing for the whole population to be tested, and requiring trained operators and special equipment. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900-1700 nm) to predict …
Corn Nitrogen Nutrition Index Prediction Improved By Integrating Genetic, Environmental, And Management Factors With Active Canopy Sensing Using Machine Learning, Dan Li, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John F. Shanahan
Corn Nitrogen Nutrition Index Prediction Improved By Integrating Genetic, Environmental, And Management Factors With Active Canopy Sensing Using Machine Learning, Dan Li, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John F. Shanahan
Department of Agronomy and Horticulture: Faculty Publications
Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen …
Evaluation Of Uav-Derived Multimodal Remote Sensing Data For Biomass Prediction And Drought Tolerance Assessment In Bioenergy Sorghum, Jiating Li, Daniel P. Schachtman, Cody F. Creech, Lin Wang, Yufeng Ge, Yeyin Shi
Evaluation Of Uav-Derived Multimodal Remote Sensing Data For Biomass Prediction And Drought Tolerance Assessment In Bioenergy Sorghum, Jiating Li, Daniel P. Schachtman, Cody F. Creech, Lin Wang, Yufeng Ge, Yeyin Shi
Department of Agronomy and Horticulture: Faculty Publications
Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, …
Soybean Response To Water: Trait Identification And Prediction, Shawn Jenkins
Soybean Response To Water: Trait Identification And Prediction, Shawn Jenkins
Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research
The rising demand for soybean [Glycine Max (L.) Merrill] taken in consideration with current climatic trends accentuates the importance of improving soybean seed yield response per unit water (WP). To further our understanding of the quantitative WP trait, a multi-omic approach was implemented for improved trait identification and predictive modeling opportunities. Through the evaluation of two recombinant inbred line populations jointly totaling 439 lines subjected to contrasting irrigation treatments, informative agronomic, phenomic, and genomic associations were identified. Across both populations, relationships were identified between lodging at maturity (r = -0.58, H = 0.86), canopy to air temperature differential …
Wheat Growth Monitoring And Yield Estimation Based On Multi-Rotor Unmanned Aerial Vehicle, Zhaopeng Fu, Jie Jang, Yang Gao, Brian Krienke, Meng Wang, Kaitai Zhong, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaojun Liu
Wheat Growth Monitoring And Yield Estimation Based On Multi-Rotor Unmanned Aerial Vehicle, Zhaopeng Fu, Jie Jang, Yang Gao, Brian Krienke, Meng Wang, Kaitai Zhong, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaojun Liu
Department of Agronomy and Horticulture: Faculty Publications
Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the …
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Department of Agronomy and Horticulture: Faculty Publications
Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …
Plantcv V2: Image Analysis Software For High-Throughput Plant Phenotyping, Malia A. Gehan, Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, Steven T. Callen, Leonardo Chavez, Andrew N. Doust, Max J. Feldman, Kerrigan B. Gilbert, John G. Hodge, J. Steen Hoyer, Andy Lin, Suxing Liu, César Lizárraga, Argelia Lorence, Michael Miller, Eric Platon, Monica Tessman, Tony Sax
Plantcv V2: Image Analysis Software For High-Throughput Plant Phenotyping, Malia A. Gehan, Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, Steven T. Callen, Leonardo Chavez, Andrew N. Doust, Max J. Feldman, Kerrigan B. Gilbert, John G. Hodge, J. Steen Hoyer, Andy Lin, Suxing Liu, César Lizárraga, Argelia Lorence, Michael Miller, Eric Platon, Monica Tessman, Tony Sax
Department of Agronomy and Horticulture: Faculty Publications
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major …