Life Sciences Commons^™

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Agricultural Research Service Weed Science Research: Past, Present, And Future, Stephen L. Young, James V. Anderson, Scott R. Baerson, Joanna Bajsa-Hirschel, Dana M. Blumenthal, Chad S. Boyd, Clyde D. Boyette, Eric B. Brennan, Charles L. Cantrell, Wun S. Chao, Joanne C. Chee-Sanford, Charlie D. Clements, F. Allen Dray, Stephen O. Duke, Kayla M. Eason, Reginald S. Fletcher, Michael R. Fulcher, Brenda J. Grewell, Erik P. Hamerlynck, Robert E. Hoagland, David P. Horvath, Eugene P. Law, Daniel E. Martin, Clint Mattox, Steven B. Mirsky, Patrick J. Moran, Rebecca C. Mueller, Vijay K. Nandula, Beth A. Newingham, Zhiqiang Pan, Lauren M. Porensky, Paul D. Pratt, Andrew J. Price, Brian G. Rector, Krishna N. Reddy, Roger L. Sheley, Lincoln Smith, Melissa C. Smith, Keirith A. Snyder, Matthew A. Tancos

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

The U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well …

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 …

Image Analysis And Machine Learning In Agricultural Research, Xinzheng Chen

Doctor of Plant Health Program: Dissertations and Student Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed.

Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could …

Identifying Early-Life Behavior To Predict Mothering Ability In Swine Utilizing Nutrack System, Savannah Millburn

Department of Animal Science: Dissertations, Theses, and Student Research

Early recognition of indicator traits for swine reproduction and longevity supports economical selection decision making. Gilt activity is a key variable impacting a sow’s herd life and productivity. The purpose of this study was to examine early- life behaviors contributing to farrowing traits including gestation length (GL), number born alive (NBA), number weaned (NW), and herd life (HL). Herd life was a binary trait representing if a gilt was culled after one parity. Beginning at approximately 20 weeks of age, video recordings were taken on 480 gilts for 7 consecutive days and processed using the NUtrack system. Activity traits include …

Causal Forest Approach For Site-Specific Input Management Via On-Farm Precision Experimentation, Shunkei Kakimoto

Department of Agricultural Economics: Dissertations, Theses, and Student Research

Estimating site-specific crop yield response to changes to input (e.g., seed, fertilizer) management is a critical step in making economically optimal site-specific input management recommendations. Past studies have attempted to estimate yield response functions using various Machine Learning (ML) methods, including the Random Forest (RF), Boosted Random Forest (BRF), and Convolutional Neural Network (CNN) methods. This study proposes use of the Causal Forest (CF) model, which is one of the emerging ML methods that comprise “Causal Machine Learning.” Unlike previous yield-prediction-oriented ML methods, CF focuses strictly on estimating heterogeneous treatment effects (changes in yields that result from changes in input …

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 …

On Line Fast Detection Of Defective Rice Flour Based On Machine Learning Algorithm, Tan Lu-Min, Feng Xin-Gang

Food and Machinery

Objective: To realize the rapid on-line detection of defective rice flour. Methods: Non-contact data acquisition of rice flour blocks through cameras, image upload and processing, obtained the contour perimeter and area, approximate contour perimeter and area, approximate contour points and radius of the contour circle. According to the characteristics of rice flour block sample data, the SVM classification algorithm was used to analyze the sample set composed of multi feature data of rice flour block. Results: Compared with five algorithms, the average accuracy of GBDT classification algorithm was 89% with elapsed time of 1.10 s. The average accuracy of KNN …

Where To Invest Project Efforts For Greater Benefit: A Framework Formanagement Performance Mapping With Examples For Potato Seed Health, C. E. Buddenhagen, Y. Xing, J. L. Andrade-Piedra, G. A. Forbes, P. Kromann, I. Navarrete, S. Thomas-Sharma, Robin A. Choudhury, K. F. Andersen Onofre, E. Schulte-Geldermann

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional …

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 …

Identifying Conifer Tree Vs. Deciduous Shrub And Tree Regeneration Trajectories In A Space-For-Time Boreal Peatland Fire Chronosequence Using Multispectral Lidar, Humaira Enayetullah, Laura Chasmer, Christopher Hopkinson, Dan Thompson, Danielle Cobbaert

Aspen Bibliography

Wildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving …

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

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: 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

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, …

Optimising The Carcass Merit Of Irish Beef Cattle Using Genetic And Non-Genetic Information At The Animal And Herd Level, David Kenny

Theses

Failure of beef carcasses to achieve desirable carcass specifications represent inefficiencies within the supply chain, namely greater carcass processing costs and the inability of the resulting primal cuts to conform to high-value market specifications. Analysis of a representative sample of prime Irish beef cattle conducted in this thesis determined that 59% of cattle fail to achieve the desired carcass specifications of the supply chain at slaughter. The objective of this thesis was to use readily available information to define strategies that could help to reduce this statistic. Firstly, the likelihood of Irish beef carcasses achieving the desired carcass specifications was …

The Potential Of Remotely Sensed Vegetation Indices For Monitoring Pasture Condition, Pouria Ramzi, Karen Holmes

Resource management technical reports

The Department of Primary Industries and Regional Development (DPIRD) is developing an integrated monitoring system using remote sensing and on-ground measurements to track pasture condition across Western Australia’s pastoral region. We extended and adapted the methods developed in the Pastoral Lease Assessment Using Geospatial Analysis (PLAGA) project (Robinson et al. 2012), which combined remotely sensed vegetation indices (VIs) with on-ground pasture condition observations to assess the potential of using different vegetation indices in a statewide condition monitoring system.

There were 6 regions in WA’s pastoral rangelands with DPIRD on-ground condition traverse points: Kimberley and Broome, Pilbara, Yalgoo and Sandstone, Goldfields, …

Automatic Identification And Monitoring Of Plant Diseases Using Unmanned Aerial Vehicles: A Review, Krishna Neupane, Fulya Baysal-Gurel

Agricultural and Environmental Sciences Faculty Research

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and …

Detection Of European Aspen (Populus Tremula L.) Based On An Unmanned Aerial Vehicle Approach In Boreal Forests, Anton Kuzmin, Lauri Korhonen, Sonja Kivinen, Pekka Hurskainen, Pasi Korpelainen, Topi Tanhuanpää, Matti Maltamo, Petteri Vihervaara, Timo Kumpula

Aspen Bibliography

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud …

A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by …

Understanding Growth Dynamics And Yield Prediction Of Sorghum Using High Temporal Resolution Uav Imagery Time Series And Machine Learning, Sebastian Varela, Taylor Pederson, Carl J. Bernacchi, Andrew D.B. Leakey

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth …

Within-Field Yield Prediction For Sugarcane And Rice Focused On Precision Agriculture Applications, Felippe Hoffmann Silva Karp

LSU Master's Theses

Food and energy security are two main topics when it comes to the on-growing world population. Rice and sugarcane play an important role in this scenario since sugarcane can be used for energy production and rice is one of major staple cereals. In this scenario, Precision Agriculture (PA) management strategies aims to improve productivity, efficiency, profitability, and sustainability, and can help agriculture to fulfill the needs of the growing population in a sustainable way. However, yield maps are essential for PA, but its adoption is still very low. Thus, the main objective of this study was to evaluate the potential …

The Calculation Methods Of Goat Trunk’S Segmentation Trajectory Based On Machine Vision And Machine Learning, Li Zhen-Qiang, Wang Shu-Cai, Zhao Shi-Da, Wang Yu-Quan

Food and Machinery

An automated segmentation method of goat trunk with machine vision and machine learning was proposed. 396 images of goat trunk were acquired randomly in a goat slaughtering plant by using two industrial cameras. 24 sets of feature parameters were extracte

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

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, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan

John E. Sawyer

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 …

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

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 …

Learning From Data: Plant Breeding Applications Of Machine Learning, Alencar Xavier

Open Access Dissertations

Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning …

Soil Salinity Study In Northern Great Plains Sodium Affected Soil, Tulsi P. Kharel

Electronic Theses and Dissertations

Climate and land-use changes when combined with the marine sediments that underlay portions of the Northern Great Plains have increased the salinization and sodification risks. The objectives of this dissertation were to compare three chemical amendments (calcium chloride, sulfuric acid and gypsum) remediation strategies on water permeability and sodium (Na) transport in undisturbed soil columns and to develop a remote sensing technique to characterize salinization in South Dakota soils. Fortyeight undisturbed soil columns (30 cm x 15 cm) collected from White Lake, Redfield, and Pierpont were used to assess the chemical remediation strategies. In this study the experimental design was …