Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

Fletcher, Reginald S.; Fisher, Daniel K.

doi:10.4236/as.2019.1011109

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Other sensors and platforms have been used with varying degrees of success to measure plant height in production fields. In a commercial soybean field, tractor-mounted ultrasonic distance sensors were used to measure plant height on four rows of soybeans simultaneously and obtained a height accuracy within 2 cm (Fletcher and Fisher, 2019). While this method was effective in a soybean field, collecting measurements 4 rows at a time is time-consuming and as maize gets much taller throughout the seasons measurements would be increasingly difficult to use this approach.…”

Section: Introductionmentioning

confidence: 99%

Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

Sweet,

Cooper,

Hirsch

et al. 2024

Preprint

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Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.

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Section: Introductionmentioning

confidence: 99%

Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

Sweet,

Cooper,

Hirsch

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The Arduino has proven to be a popular and powerful development tool for researchers and scientists, as well as hobbyists, artists, and other makers. Electronic devices and instrumentation have been developed in many different disciplines, such as agriculture [4] [5] [6] [7] [8], irrigation and water management [9] [10] [11] [12], robotics [13] [14] [15], scientific and environmental studies [16] [17] [18] [19], and multipurpose laboratory and field data collection [20] [21] [22] [23] [24]. Development using the Arduino project has resulted in many successful applications, and the Arduino hardware and software environment continues to expand, include additional features, and become more powerful [25].…”

Section: Introductionmentioning

confidence: 99%

Python Software Integrates with Microcontrollers and Electronic Hardware to Ease Development for Open-Source Research and Scientific Applications

Fisher¹,

Fletcher²,

Anapalli³

2021

AIT

Self Cite

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Many options exist for developing and implementing monitoring systems for research and scientific applications. Commercially, available systems and devices, however, are usually built using proprietary tools and programming instructions, and often offer limited flexibility for end users. The use of open-source hardware and software has been embraced by the research and scientific communities and can be used to target unique data and information requirements. Development based on the Arduino microcontroller project has resulted in many successful applications, and the Arduino hardware and software environment continues to expand and become more powerful but can be intimidating for users with limited electronics or programming experience. The open-source Python language has gained in popularity and is being taught in schools and universities as an introduction to computer programming and software development due to its simple structure, ease of use, and large standard library of functions. A project called CircuitPython was developed to extend the use of Python to programming hardware devices such as programmable microcontrollers and maintains much of the original Python language and features, with additional support for accessing and controlling microcontroller hardware. The objective of the work reported here is to discuss the CircuitPython programming language and demonstrate its use in the development of research and scientific applications. Several open-source sensing and monitoring systems developed using open-source hardware and the open-source CircuitPython programming language are presented and described.

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Leveraging Big Data to Preserve the Mississippi River Valley Alluvial Aquifer: A Blueprint for the National Center for Alluvial Aquifer Research

Nelson¹,

Quintana-Ashwell

Delhom³

et al. 2022

Land

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The challenge of a depleting Mississippi River Valley Alluvial Aquifer (MRVAA) requires reducing groundwater withdrawal for irrigation, increasing aquifer recharge, and protecting water quality for sustainable water use. To meet the challenge, the National Center for Alluvial Aquifer Research (NCAAR) is oriented towards producing scientific work aimed at improving irrigation methods and scheduling, employing alternative water sources, and improving crop management and field practices to increase water use efficiency across the region. Big data is key for NCAAR success. Its scientists use big data for research in the form of various soil, weather, geospatial, and water monitoring and management devices to collect agronomic or hydrogeologic data. They also produce, process, and analyze big data which are converted to scientific publications and farm management recommendations via technology transfer. Similarly, decision tools that would help producers leverage the wealth of data they generate from their operations will also be developed and made available to them. This article outlines some of the many ways big data is intertwined with NCAAR’s mission.

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Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors

Cited by 3 publications

References 15 publications

Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

Python Software Integrates with Microcontrollers and Electronic Hardware to Ease Development for Open-Source Research and Scientific Applications

Leveraging Big Data to Preserve the Mississippi River Valley Alluvial Aquifer: A Blueprint for the National Center for Alluvial Aquifer Research

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