Browsing by Author "Shafian, Sanaz"

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    Assessing Spray Deposition and Weed Control Efficacy from Aerial and Ground Equipment in Managed Turfgrass Systems
    Koo, Daewon (Virginia Tech, 2024-05-24)
    There is a growing interest in agricultural spray drone (ASD) use for herbicide application in managed turfgrass systems, which historically has precluded aerial application. Considering pesticide deposition accuracy is of utmost importance in managed turfgrass systems, a thorough examination of factors that influence ASD spray deposition patterns is needed. A python-based spray deposition pattern analysis tool, SprayDAT, was developed to estimate spray quality utilizing a cost-effective continuous sampling technique involving digital soand spectrophotometric analysis of blue colorant stains on white Kraft paper. This technique cost 0.2 cents per USD spent on traditional water-sensitive paper (WSP) allowing for continuous sampling necessary for the highly variable deposition patterns delivered by an ASD. SprayDAT conserved droplet densities and more accurately detected stain objects compared to a commonly utilized software, DepositScan, which overestimated stain sizes. However, droplet density exhibited an upper asymptote at 22% stain cover when relating volume median diameter (VMD) due to increasing overlap of stain objects. Spread factor of blue colorant stains was fit to a 2-parameter power equation when compared across six discrete droplet sizes between 112 and 315 µm when droplets were captured in a biphasic solution of polydimethylsiloxane of 100 cSt over 12,500 cSt viscosities. Cumulative digitally assessed stain objects underestimated application volume 270% when compared to the predicted output based on flow rate, coverage, and speed. SprayDAT incorporates a standard curve based on colorant extraction and spectrophotometric analysis to correct this error such that total stain area accurately estimates application volume to within 9%. This relationship between extracted colorant and total stain area, however, is dependent on droplet size spectra. SprayDAT allows users to customize standard curves to address this issue. Using these analysis techniques, continuous sampling of a 29.3-m transect perpendicular to an ASD or ground sprayer spray swath resolved that increasing ASD operational height increases drift and effective swath width while effective application rate, total deposition, and smooth crabgrass control by quinclorac herbicide decreases. Deposition under the ASD was heterogeneous as the coefficient of variation (CV) within the targeted swath exceeded 30% regardless of operational height. At higher operational heights, relative uniformity of spray pattern was improved but droplet density at 11.7 m away from the intended swath edge was up to four times greater and total spray deposited was up to 60% reduced at the highest heights. For each 1-m increase in ASD operational height, 6% of the deposited spray solution, 11% of the effective application rate within the targeted swath, and 7% of smooth crabgrass [Digitaria ischaemum (Schreb.) Schreb. ex Muhl.] population reduction declined. Subsequent studies suggested that total deposition loss with increasing operational height of ASD were likely due to droplet evaporation. Discrete-sized droplets subjected to a 5-m fall in a windless environment exhibited a sigmoidal relationship where 98% volume of 135-µm droplets and approximately 67% volume of 177 – 283 µm diameter droplets evaporated. Addition of drift reduction agents (DRAs) or choosing different nozzle types altered the initial droplet density generated by a flat-fan nozzle. Regardless of DRA additions or nozzle replacement, the distance required to lose 50% of small droplets (< 150 µm diameter) was 6.6 m. Air induction nozzles and DRA admixtures also conserved smooth crabgrass control across 2- and 6-m operational heights, where control was reduced at the 6-m height with a flat fan nozzle without DRA. Spray deposition pattern analysis for multipass ASD and ground applications was conducted by utilizing nighttime UV-fluorescence aerial photography and weed infestation counts in a digitally overlaid grid. Results show that under-application across all devices was consistent and averaged 12%, whereas at least 14% more over-application on the targeted area was observed for ASD, regardless of equipped nozzle types, compared to a ride-on sprayer. Drift also occurred at least 3 times more for ASD application than for a ride-on sprayer and a spray gun sprayer. Using smooth crabgrass infestation annotated from aerial images could not consistently resolve the spatial variability evident in UV-fluorescent imagery presumably due to the innate variability in weed populations. Analysis using SprayDAT revealed insights into factors affecting ASD spray deposition, such as operational height impacting drift, effective swath width, and herbicide efficacy, highlighting the tool's utility in optimizing aerial herbicide applications in turfgrass management. Data suggest that the lowest ASD operational height should be employed to partially mitigate drift and droplet evaporation while improving weed control. Lower operational heights, however, reduce effective swath width and increase heterogeneity of the deposition pattern. Future research should evaluate possible engineering controls for these problems.
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    Developing remote sensing approaches for integrated pest and pollinator management in turfgrass
    Bradley, Shannon Grace (Virginia Tech, 2023-09-06)
    Golf courses can expand hundreds of acres, making scouting for both pests and beneficial insect populations a time-consuming task. Scouting for insects is labor-intensive, potentially damaging, but is an integral part of an integrated pest and pollinator management (IPPM) plan. Virginia golf courses are currently using remote sensing and light reflectance to detect non-insect pests in turfgrass. This thesis aims to develop remote sensing and light reflectance methods to aid in a turfgrass IPPM plan, to document the phenology of ABW weevil (Listronotus maculicollis Kirby, Coleoptera: Curculionidae, ABW), and to catalogue pollinator-friendly out-of-play areas. Light reflectance, the measurement of the amount of light reflected, of plants can be used as a proxy for the health of a plant. The light reflectance of turfgrass affected by ABW stress and plants in the out-of-play areas of golf courses was collected proximally and remotely, using a backpack spectrometer and an unmanned aerial vehicle (UAV), respectively. Mathematical light reflectance indices were applied and compared to insect populations in both areas to determine the correlation. The Normalized Difference Vegetation Index (NDVI), which uses red and near-infrared wavelengths to indicate stress, was found to highlight ABW stressed turfgrass. The Structure Intensive Vegetation Pigment Index (SIPI), which uses red and green wavelengths to highlight flowering plants, was found to highlight potential pollinator- friendly habitats in out-of-play areas. When applied to flights, NDVI could help in the targeted application of insecticides to combat the annual bluegrass weevil, therefore reducing their presence in the environment. The use of SIPI could highlight potential pollinator friendly habitats and therefore assist superintendents in the development of their IPPM plan.
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    Digital Agriculture and Intelligent Farming Business Using Information and Communication Technology: A Survey
    El Idrissi, Mohammed; El Beqqali, Omar; Riffi, Jamal; Shamshiri, Redmond R.; Shafian, Sanaz; Hameed, Ibrahim A. (IntechOpen, 2022)
    Adopting new information and communication technology (ICT) as a solution to increase agricultural productivity and achieve food security becomes more urgent than before, particularly with the demographical explosion and the exponential increase of population worldwide. In this survey, we analyze the literature in the last decade to examine the existing fog/edge computing architectures adapted for the smart farming domain and identify the most relevant challenges resulting from the integration of IoT and fog/edge computing platforms. On the other hand, we describe the status of Blockchain usage in intelligent farming as well as the most challenges 36 this promising topic is facing. The relevant recommendations and researches needed in Blockchain topic to enhance intelligent farming sustainability are also highlighted in this paper. This survey provides also an overview of the IoT middleware dedicated to dealing with virtual sensor data. It is found through the examination that the adoption of ICT in the various farming processes helps to increase productivity with low efforts, and costs. Several challenges are faced when implementing such solutions, they are mainly related to the technological development, energy consumption, and the complexity of the environments where the solutions are implemented. Despite these constraints, it is certain that shortly several farming businesses will heavily invest to introduce more intelligence into their management methods. Furthermore, the use of sophisticated deep learning and Blockchain algorithms may contribute to the resolution of many recent farming issues.
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    Does NDVI consistently assess plant response to herbicides?
    Koo, Daewon; Vahidi, Milad; Gonçalves, Clebson G.; Peppers, John M.; Shafian, Sanaz; Askew, Shawn D. (2022-11-08)
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    Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques
    Vahidi, Milad; Shafian, Sanaz; Thomas, Summer; Maguire, Rory O. (MDPI, 2023-10-18)
    Quantifying the forage biomass in pastoral systems can be used for enhancing farmers’ decision-making in precision management and optimizing livestock feeding systems. In this study, we assessed the feasibility of integrating Sentinel-1 and Sentinel-2 satellite imagery with machine learning techniques to estimate the aboveground biomass and forage quality of bale grazing and sacrificed grassland areas in Virginia. The workflow comprised two steps, each addressing specific objectives. Firstly, we analyzed the temporal variation in spectral and synthetic aperture radar (SAR) variables derived from Sentinel-1 and Sentinel-2 time series images. Subsequently, we evaluated the contribution of these variables with the estimation of grassland biomass using three machine learning algorithms, as follows: support vector regression (SVR), random forest (RF), and artificial neural network (ANN). The quantitative assessment of the models demonstrates that the ANN algorithm outperforms the other approaches when estimating pasture biomass. The developed ANN model achieved an R2 of 0.83 and RMSE of 6.68 kg/100 sq. meter. The evaluation of feature importance revealed that VV and VH polarizations play a significant role in the model, indicating the SAR sensor’s ability to perceive changes in plant structure during the growth period. Additionally, the blue, green, and NIR bands were identified as the most influential spectral variables in the model, underscoring the alterations in the spectrum of the pasture over time.
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    Evaluation of Rotational Bale Grazing as an Alternative Winter-Hay Feeding System for Beef Cows
    Thomas, Summer Payge (Virginia Tech, 2024-05-28)
    Traditional methods of winter hay feeding for beef cattle often entail numerous challenges, including high labor demands, high fuel consumption, soil compaction, limited nutrient dispersion, heightened nutrient runoff risks, low forage yields, and nitrogen loss from manure. Rotational bale grazing (RBG) offers an innovative approach to winter hay feeding by strategically positioning hay bales on pasture prior to the onset of winter feeding, then allowing controlled access to the bales. However, its feasibility in the temperate climate of the Southeastern U.S. remains uncertain. Therefore, this dissertation, conducted over two years at the Shenandoah Valley Agricultural Research and Extension Center in Raphine, VA, aimed to assess the feasibility of implementing RBG in Virginia. Three pre-established sacrifice paddocks (SP) and three five- paddock rotations for RBG treatments were utilized, with cattle being winter hay-fed for approximately 60 days. The study commenced with soil grid sampling across Novel Endophyte Tall Fescue (Schendonorous arundinaceous) pastures, strategically placing hay bales in areas with the lowest Mehlich 1- phosphorus (P). Changes in Mehlich 1-P, Mehlich 1-potassium (K), water-soluble P (WSP), nitrate (NO3-N), and pH were monitored over a two-year RBG implementation period. The results indicated that bale placement did not significantly alter the spatial distribution of Mehlich 1-P concentrations, but consistently influenced Mehlich 1-K distribution. The WSP concentrations remained consistent with bale placement but decreased without. Bale placement had no significant effect on NO3-N or pH. Furthermore, this study investigated the effects of RBG on nutrient and sediment runoff in comparison to traditional SP. Artificial rainfall simulations were conducted on a SP, RBG first paddock grazed in the rotation (RBG first), and RBG last paddock grazed in the rotation (RBG last). The most significant differences were observed between the RBG first and last treatments, with forage presence in the RBG first paddock contributing to diminished runoff volume and nutrient load. Notably, the SP treatment showed no significant difference from the RBG treatment, likely due to many years of manure deposition increasing soil organic matter and water infiltration. The findings suggested that an RBG paddock grazed last in the rotation may lead to increased runoff volumes, sedimentation, and nutrient concentrations compared to an RBG paddock grazed first in the rotation. To understand the impact of RBG on forage biomass recovery, spring forage biomass was measured using ground manual samples and drone imagery. Manual samples and drone flights were conducted three times post-winter hay feeding. While the SP system exhibited the highest biomass recovery in high animal impact areas, RBG showed numerically higher biomass by the third sampling date in low animal impact areas. Drone imagery showcased potential for biomass estimation, but processing of drone images took excessive time and rendered it less feasible compared to manual samples.
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    The Genetic Architecture of Grain Quality and its Temporal Relationship with Growth and Development in Winter Malting Barley (Hordeum vulgare)
    Loeb, Amelia (Virginia Tech, 2023-06-26)
    This thesis explores the genetic architecture of malting quality within the Virginia Tech barley breeding program, and discusses implications for imposing selection on complex traits that are difficult to phenotype. Malting quality measures are destructive, and can not be performed before selection must be made for advancement of breeding lines in winter barley. A growing body of evidence suggests that malt quality is influenced by malting regime, growing environment, line genotype, and the interactions between them. We aim to better understand the genetic effect on malt quality in two manners: first, as it relates to the genetic architecture regulating malt quality parameters, and second the relationship between genetic growth patterns to end-use malting traits. This study included two years of breeding trial data of two and six-row winter malt barley across two locations. Results of a genome-wide association scan and genomic prediction of malt quality traits indicated that they are largely quantitative traits with complex inheritance. Previous studies have identified quantitative trait loci and genes regulating malt quality traits in markedly different germplasm. Heritability of traits ranged from 0.27 to 0.72, while mean predictive abilities ranged from 0.45 to 0.74. Thus, selection on genomic estimated breeding values (gEBVs) should perform similarly to selection on single phenotypic observations of quality, but can be done within the same season. This indicates that genomic selection may be a viable method to accelerate genetic improvement of malting quality traits. The use of gEBVs requires that lines be genotyped with genome-wide markers, somewhat limiting the number of candidate individuals. Selection on growth and development traits genetically correlated with quality measures could allow for selection among a much greater number of candidates if high-throughput phenotypes can be collected on many ungenotyped indivduals. Growth and development was quantified by the near-infrared vegetation index (NDVI) extracted from aerial images captured from multiple time points throughout the growing season. Estimates of genetic correlation identified time points throughout the season when quality traits are related to growth and development. We demonstrated that aerial imagery can discern growth patterns in barley and suggest ways it can be incorporated into the breeding pipeline.
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    Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence
    Shamshiri, Redmond R.; Hameed, Ibrahim; Thorp, Kelly; Balasundram, Siva; Shafian, Sanaz; Fatemieh, Mohammad; Sultan, Muhammad; Mahns, Benjamin; Samiei, Saba (IntechOpen, 2021-06-16)
    Automation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dual-core 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments.
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    Hemp Seed Yield Responses to Nitrogen Fertility Rates
    Podder, Swarup; Shafian, Sanaz; Thomason, Wade E.; Wilson, T. Bain; Fike, John H. (MDPI, 2024-04-11)
    Industrial hemp (Cannabis sativa L.) holds promise as a crop for more sustainable supply chains given its potential as a source of high-strength fibers, adsorbents, and nutrient-dense feedstuffs. Developing nutrient management guidelines for hemp will be an important part of optimizing the crop’s sustainability attributes. This study measured hemp seed yield in response to N fertilization rate (0, 60, 120, 180, and 240 kg N ha−1). Treatments were tested with four hemp cultivars (‘Joey’ and ‘Grandi’ in 2020, 2021, and 2022 and ‘NWG 2463’ and ‘NWG 4113’ in 2023) in Virginia. Nitrogen input influenced (p ≤ 0.0177) seed yield in all four experimental years, although the pattern of response varied substantially. In 2020, following delayed seeding, hemp showed a weak quadratic (p = 0.0113) response to N inputs, with peak yield (1640 kg ha−1) occurring with 120 kg N ha−1. In 2021, hemp displayed a strong linear (p < 0.0001) response to N inputs, with the highest seed yield (2510 kg ha−1) at 240 kg N ha−1. In 2022, a season characterized by low precipitation and high weed pressure, a weak, linear (p = 0.0111) response to the N rate was observed. The greatest seed yield (380 kg ha−1) was again observed with 240 kg N ha−1. In 2023, weed pressure remained an issue, but the response to N was strong and linear (p < 0.0001), with the greatest seed yield (831 kg ha−1) again measured at 240 kg N ha−1. These findings indicate hemp can be quite responsive to N inputs but that the magnitude of response is sensitive to other factors such as available soil moisture, weed pressure, and growing period.
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    Industrial hemp agronomic management for grain, fiber, and forage
    Podder, Swarup (Virginia Tech, 2023-09-12)
    This research involved testing several aspects of industrial hemp (Cannabis sativa L.) production, including the impact of tillage on seed and fiber production, optimal harvest time for seed yield and quality, the response of seed yield to nitrogen fertility rates, and the potential of hemp as a forage crop. A three-year study was conducted in Blacksburg and Orange of Virginia State to assess the effects of tillage management and production systems (e.g., seed, dual, and fiber) on hemp establishment and productivity. Two cultivars, Joey (a dual-purpose variety) and EcoFibre (bred specifically for fiber), were planted into seedbeds prepared with conventional tillage and no-till management. The cultivar Joey, lower plant populations under seed production systems resulted in taller plants (P = 0.0002) compared to the dual-purpose production systems in 2020. Greater plant heights (P < 0.0001) with fiber production systems in 2021 and 2022 were due to differences between cultivars and their time of flowering. Conventional tillage resulted in greater (P ≤ 0.0161) plant populations than no-tillage for all production systems in each year, and this response was more pronounced with fiber management in 2020 (tillage × production systems interaction; P = 0.0007). Greater (P < 0.001) yields with fiber systems observed in 2021 and 2022 were largely driven by the more productive EcoFibre cultivar. Despite treatment differences in population density, biomass and seed yields varied less by tillage management and production systems. Lower plant population density was associated with greater biomass and seed yields per plant. However, for desired fiber quality and mechanical harvest feasibility, a higher plant population density is recommended. A second study aimed to determine the optimum harvest time for seed yield of two hemp cultivars. 'Joey', and 'Grandi,', were established in Blacksburg and Orange, Virginia in mid-May/early June of 2021 and 2022. The experiment was conducted as a randomized complete block design with a repeated measurement arrangement and four replicates. Plants were harvested four times at one-week intervals starting in mid-summer. Harvest date significantly affected seed yield, with the response differing by cultivar (cultivar × date interaction; P = 0.001) in 2022 at the Orange site. In Blacksburg, seed yields were similar for the two cultivars and greatest at the second harvest each season (July 22, 2021, and July 25, 2022), although they were substantially lower in 2022 due to drought (1750 vs. 480 kg ha-1; P < 0.0001). In Orange, in 2021, as planting occurred late, harvests were also deferred until August 17, and seed yields were greatest at this first harvest (1180 kg ha-1; P<0.0001). In 2022, yields at the Orange location were highest for Grandi at the first harvest (July 21; 1510 kg ha-1) and for Joey at the second harvest (July 28; 1280 kg ha-1) (Harvest Time by Cultivar interaction, P = 0.0010). Over the subsequent weeks of harvest, yields drastically declined (16 to 41% in 2021 and 27 to 47% in 2022 in Blacksburg; 52% to 91% in 2021 and 28% to 65% in 2022 in Orange, compared to the highest yield). Harvest timing is critical to achieving optimum seed yield, and it varies with cultivar, eco-physiographic location, and weather (e.g., rainfall). Fatty acids (FA) varied by cultivar, location, and harvest timing, but patterns of response were not consistent across FA. Gamma-linolenic (P ≤ 0.002) and oleic acids (P ≤ 0.023) were generally greater in Joey, with greater arachidic acid (P ≤ 0.013) concentrations in Grandi. Stearidonic acid concentrations declined with later harvest date in Orange location (P ≤ 0.0034). A third study aimed to measure hemp's response to different N rates and to determine the ability to predict plant N content and seed yield based on UAV-based multispectral imagery. Two hemp cultivars, 'Joey' and 'Grandi', were planted and five N rates (0, 60, 120, 180, 240 kg N ha-1) were tested in Blacksburg, Virginia in 2020, 2021, 2022. Aerial image acquisition occurred at three different growth stages in 2021 using dji M 300 drones mounted with multispectral sensors. Red/Blue index (R2=0.89), near-infrared (NIR) band (R2=0.84) and Enhanced vegetation index (EVI) (R2=0.81) were better predictors of N content in leaf samples than other vegetation indices that were evaluated. Green normalized difference vegetation index (GNDVI) was the better predictor of hemp seed yield (R2=0.58) than other evaluated vegetation indices. The seed yield of hemp was influenced (P ≤ 0.0177) by the N input in all three experimental years. In 2020, seed yield did not increase steadily with the increase of N rate; the highest seed yield, 1640 kg ha-1, was observed at 120 kg N ha-1. In 2021, maximum seed yield of 2500 kg ha-1 occurred at the maximum N rate (240 kg N ha-1). In 2022, a weak response to N rate was observed; maximum seed yield was 380 kg ha-1, again at 240 kg N ha-1. The overall growth of the hemp plants was affected by limited rainfall and weed pressures in 2022, leading to a significant reduction in seed yield. Response to N rate will vary depending on other factors such as available soil moisture during the growing season, weed pressure, and growing period. A fourth study examined the yield and nutritive value of three hemp cultivars, 'Grandi', 'Joey', and 'EcoFibre' as potential forage crops when harvested at weekly intervals in Blacksburg, VA. The greatest biomass and TDN yields across cultivars were 3.17 Mg ha-1 and 2.08 Mg ha-1 respectively, at two months after establishment in 2021. In the dry 2022 season, biomass and TDN yield were 1.9 Mg ha-1 and 1.03 Mg ha 1, respectively, two months after establishment. Hemp nutritive value measures varied by cultivar and harvest time (P < 0.05). Depending on the cultivar and harvest time, hemp plant biomass contained 13 to 32% CP, 22 to 45% NDF, 20 to 38% ADF, 4 to 9% lignin, and 52 to 80% TDN (cultivar × time interaction; P < 0.05). Hemp CP and TDN decreased gradually with maturation while ADF, NDF, and lignin increased (P<0.0001); however, this decline with maturity did not appear as severe as occurs with many other forages. These preliminary results suggest that hemp has the potential to be used as a forage crop. More research is needed to address hemp management and utilization, including field establishment and production, harvest timing for optimum tonnage and forage quality, and animal intake and performance studies. These findings provide new insights into industrial hemp production in the mid-Atlantic region of the United States. Optimal tillage practices, precise harvest timing, appropriate N fertility rates, and proper management techniques all are crucial for maximizing hemp seed and fiber production and quality. Furthermore, hemp shows promise as a forage crop with its adaptability and favorable nutritional properties. Further research is warranted to refine cultivation techniques, improve crop quality, and explore the full potential of hemp in various industries.
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    Modeling Carbon Uptake of Dryland Maize Using High Resolution Satellite Imagery
    Menefee, Dorothy; Rajan, Nithya; Shafian, Sanaz; Cui, Song (Frontiers, 2022-03)
    Quantifying carbon uptake or gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crops. The availability of high-resolution remote sensing data can significantly improve GPP estimation of small-scale agricultural fields. Multispectral satellite data with 3-m spatial resolution and frequent global coverage are available from the PlanetScope network of satellites. However, this data remains largely unexplored for studying the carbon dynamics of agroecosystems. The overarching goal of this study was to develop a simple empirical method for quantifying the GPP of dryland maize (Zea mays L.) using remotely sensed vegetation indices along with in-situ measurements of photosynthetically active radiation and leaf area index by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the two-band enhanced vegetation index (EVI2). This study was conducted over a three-year period from 2017 to 2019 in East-Central Texas. A total of 12 GPP prediction models were developed using individual yearly data and were used for predicting GPP of the other 2 years. Predicted maize GPP values were then compared against tower-based GPP. The NDVI models were the least successful in predicting GPP and had the highest root mean square error (average: 10.1 3 gC m−2; maximum: 26.3 gC m−2). Models based on SAVI performed especially well with error ranging from 0.05 to 0.94 gC m−2. The slope of the regression between SAVI-based estimated GPP and measured GPP was not different from 1.0 in all combinations of years. The success of the SAVI-based GPP models for predicting dryland maize carbon uptake indicates that it was the least affected vegetation index by changing soil background condition in this row cropping system.
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    Pasture Biomass Estimation Using Ultra-High-Resolution RGB UAVs Images and Deep Learning
    Vahidi, Milad; Shafian, Sanaz; Thomas, Summer; Maguire, Rory O. (MDPI, 2023-12-13)
    The continuous assessment of grassland biomass during the growth season plays a vital role in making informed, location-specific management choices. The implementation of precision agriculture techniques can facilitate and enhance these decision-making processes. Nonetheless, precision agriculture depends on the availability of prompt and precise data pertaining to plant characteristics, necessitating both high spatial and temporal resolutions. Utilizing structural and spectral attributes extracted from low-cost sensors on unmanned aerial vehicles (UAVs) presents a promising non-invasive method to evaluate plant traits, including above-ground biomass and plant height. Therefore, the main objective was to develop an artificial neural network capable of estimating pasture biomass by using UAV RGB images and the canopy height models (CHM) during the growing season over three common types of paddocks: Rest, bale grazing, and sacrifice. Subsequently, this study first explored the variation of structural and color-related features derived from statistics of CHM and RGB image values under different levels of plant growth. Then, an ANN model was trained for accurate biomass volume estimation based on a rigorous assessment employing statistical criteria and ground observations. The model demonstrated a high level of precision, yielding a coefficient of determination (R2) of 0.94 and a root mean square error (RMSE) of 62 (g/m2). The evaluation underscores the critical role of ultra-high-resolution photogrammetric CHMs and red, green, and blue (RGB) values in capturing meaningful variations and enhancing the model’s accuracy across diverse paddock types, including bale grazing, rest, and sacrifice paddocks. Furthermore, the model’s sensitivity to areas with minimal or virtually absent biomass during the plant growth period is visually demonstrated in the generated maps. Notably, it effectively discerned low-biomass regions in bale grazing paddocks and areas with reduced biomass impact in sacrifice paddocks compared to other types. These findings highlight the model’s versatility in estimating biomass across a range of scenarios, making it well suited for deployment across various paddock types and environmental conditions.
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    Spring wheat yield and grain quality response to nitrogen rate
    Walsh, Olga S.; Marshall, Juliet; Nambi, Eva; Shafian, Sanaz; Jayawardena, Dileepa; Jackson, Chad; Lamichhane, Ritika; Owusu Ansah, Emmanuella; McClintick-Chess, Jordan (Wiley, 2022-07-01)
    Nitrogen (N) is the most limiting nutrient in cereal production, yet its use efficiency remains very low at only 35%. Nutrient use efficiency (NUE) is crucial for increasing crop yield and quality while reducing fertilizer inputs and minimizing environmental damage. Optimum N rates that maximize yield without reducing NUE have been found to vary from location to location. This field study assessed the effect of N rates on the yield and quality of spring wheat (Triticum aestivum L.) at five locations in southern Idaho in 2015–2017. Nitrogen was applied as urea (46–0–0) immediately after planting at five rates: 0, 84, 168, 252, and 336 kg ha–1. Nitrogen application improved grain quality (increased protein) even when no increase in yield was noted. Nitrogen use efficiency and N uptake were affected by N rate at only 2 and 4 of 14 site-years, respectively. These observations highlight the challenging task of pinpointing the appropriate N rates for optimizing wheat yield, grain protein, N uptake and NUE; and the importance of adjusting N rates based on location, year, and prevalent environmental conditions.
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    UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds
    Li, Minhui; Shamshiri, Redmond R.; Schirrmann, Michael; Weltzien, Cornelia; Shafian, Sanaz; Laursen, Morten Stigaard (MDPI, 2022-01-26)
    Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90. The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions.
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    UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates
    Walsh, Olga S.; Nambi, Eva; Shafian, Sanaz; Jayawardena, Dileepa M. M.; Ansah, Emmanuella Owusu; Lamichhane, Ritika; McClintick-Chess, Jordan R. R. (Wiley, 2023-03)
    The accuracy of the traditional soil and plant-based techniques for assessing sugarbeet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet (Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)-based data collection for in-season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to 50%, in both years. At higher N rates, 50% level produced higher yields. At each N level, application of water at 100% level resulted in lower ERS, compared to 50%. The UAV NDVI was strongly correlated with root yield and ERS. The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting) compared to June (40 days after planting). Estimating the yield and ERS potential in late June/early July and topdressing the crop before the end of July may help to improve N use efficiency while optimizing sugarbeet production.
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    Wheat yield and protein estimation with handheld- and UAV-based reflectance measurements
    Walsh, Olga S.; Marshall, Juliet; Jackson, Chad; Nambi, Eva; Shafian, Sanaz; Jayawardena, Dileepa M.; Lamichhane, Ritika; Owusu Ansah, Emmanuella; McClintick-Chess, Jordan R. (Wiley, 2022-09-27)
    Precision agriculture provides efficient means of obtaining real-time data to guide nitrogen (N) management based on predicted crop profitability. This study was conducted to assess the efficacy of using in-season measurements (plant height, biomass weight, biomass N, soil plant analysis development [SPAD], GreenSeeker [GS] normalized difference vegetative index [NDVI], and unmanned aerial vehicle [UAV] NDVI) at Feekes 5 (tillering) and Feekes 10 (anthesis) to estimate wheat (Triticum aestivum L.) yield and protein. The secondary aim was to determine whether the accuracy of yield and protein prediction varies by wheat class and cultivar. Six cultivars—hard red spring (HRS) wheat ‘Jefferson’ and ‘SY Basalt’, hard white spring (HWS) wheat ‘Dayn’ and ‘UI Platinum’, and soft white spring (SWS) wheat ‘Seahawk’ and ‘UI Stone’—were planted at two locations in Idaho in 2018–2020. Plots were arranged in a randomized complete block design with four replications with each cultivar evaluated at seven N rates (0, 50, 100, 150, 200, 250, and 300 kg N ha–1). The determination of the Pearson correlation coefficients revealed that all parameters were linearly correlated with yield except for SPAD at Feekes 5 and biomass weight at Feekes 10. Although estimation of in-season grain protein remains a challenge, NDVI was strongly correlated with yield especially at Feekes 5. The accuracy of yield prediction was similar for all wheat classes. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. Both hand-held and aerial-based spectral measurements could be used to prescribe N rates to be applied during tiller formation when wheat yield can be optimized.