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Subject: remote sensing

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    Using UAS Images to Remotely Estimate Soil Moisture Content in the Red River Valley
    (North Dakota State University, 2021) Mack, Talon Clancy
    Accurate measurements of soil moisture in a timely manner are necessary in making critical management decisions, but it is often very difficult to obtain. Even though soil moisture can be measured on the ground using various methods, or estimated via satellite imagery, soil moisture conditions at a field scale can be more beneficial. Since Unmanned Aircraft System (UAS) technology has become an effective tool in many different ways for producers and researchers, the spatial and temporal gaps between the ground and the satellite approaches can be fulfilled with the use of UAS. In this study, multi-spectral images were collected over an agricultural field in the Red River Valley from an UAS platform. Using this data, the soil moisture content was calculated, and a soil moisture map was developed. The remotely sensed soil moisture was then compared to the in-situ field moisture measurements to gage the soil moisture mapping accuracy.
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    Rangeland Forage Growth Prediction, Logistics, Energy, and Economics Analysis and Tool Development Using Open-Source Software
    (North Dakota State University, 2022) Navaneetha Srinivasagan, Subhashree
    Forage availability was crucial for livestock production across the United States. Rangelands occupied vast areas 31 % of land and were the primary source of forage for livestock. However, extreme climatic conditions such as drought affect rangeland forage production and pose a serious threat to the rangeland enterprise. This increases the need to monitor forage in vast rangelands and adapt to other measures such as cultivating or buying forage to balance demand and supply. Despite this need, resources (studies and tools) on rangeland forage monitoring and existing forage production, handling, and economics were scattered and scarce. Therefore, a comprehensive systematic literature review was performed to gather the current understanding of the technology and resources used for monitoring and economics of forage production. Remote sensing technologies were widely used in recent research for their ability to scout vast areas frequently and machine learning (ML) in successfully comprehending complex relationships. Forage production economics was predominantly available for alfalfa forage crop, but other crops and bale collection logistics during production were ignored. Bale collection using conventional tractor carrying 1 and 2 bales/trip (BPT) and automatic bale picker (8-23 BPT) was simulated mathematically and analyzed with open-source R software using realistic equipment turning scenarios. Fuel consumption based on aggregation distance for ABP decreased on average by 72 % and 53 % compared to the tractor with 1 and 2 BPT. A web-based calculator tool was developed using open-source HTML, CSS, and JavaScript software for forage economic analysis including more than 10 varieties of forage crops involving the economics of bale collection (tractor and ABP). Pasture biomass yield prediction was performed with R software using vegetation index (VI) and climate data through ML approaches. Recursive feature selection (RFE) and random forest (RF) model for forage yield emerged as the best methodology based on accuracy. A web-based interactive tool was developed using the Shiny package in R to accommodate “field-specific,” pasture-scale inputs for predicting biomass yield. In conclusion, these successful results demonstrate the possibility of using open-source software for simulating logistics, developing models, and building tools for forage monitoring and analyzing the economics of forage production.
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    Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms
    (North Dakota State University, 2021) Pathak, Harsh
    Evaluating plant stand count or classifying weeds by manual scouting is time-consuming, laborious, and subject to human errors. Proximal remote sensed imagery used in conjunction with machine vision algorithms can be used for these purposes. Despite its great potential, the rate of using these technologies is still slow due to their subscription cost and data privacy issues. Therefore, in this research, open-source image processing software, ImageJ and Python that support in-house processing, was used to develop algorithms to evaluate stand count, develop spatial distribution maps, and classify the four common weeds of North Dakota. A novel sliding and shifting region of interest method was developed for plant stand count. Handcrafted simple image processing and machine learning approaches with shape features were successfully employed for weed species classification. Such tools and methodologies using open-source platforms can be extended to other scenarios and are expected to be impactful and helpful to stakeholders.