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1. Evaluating Spatial Effects Induced by Alternative On- Farm Trial Experimental Designs with Cross-regressive Variables Using Monte Carlo MethodsThe goal of this research was to adapt spatial regression methods to on-farm trials in a farm management context. Different experimental designs and statistical analysis methods are tested with site-specific data under a range of spatial autocorrelation levels using Monte Carlo simulation techniques. Simulations indicated that data usable for farm management decision making could be gathered from limited replication experimental designs if that data were analyzed with the appropriate spatial ... T.W. Griffin, R.J. G.m. florax, J. Lowenberg-deboer |
2. A New Approach for Quantitative Land Suitability Evaluation Using Geostatistics, Remote Sensing (Rs) and Geographic Information System (Gis)The objective of this study was to incorporate geostatistics, remote sensing and geographic information system methods due to improving the quantitative land suitability assessment in Arsanjan plain, southern Iran. The primary data was collected from 85 soil samples from tree depths (030, 3060 and 6090 cm) and the secondary information from remotely sensed data “LISSIII receiver from IRSP6 satellite”. In order to identify the spatial dependence of soil imp... M. Baghernejad, M. Emadi |
3. Use of a Cropping System Model for Soil-specific Optimization of Limited WaterIn the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irriga... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen |
4. Development of an Airborne Remote Sensing System for Aerial ApplicatorsAn airborne remote sensing system was developed and tested for recording aerial images of field crops, which were analyzed for variations of crop health or pest infestation. The multicomponent system consists of a multi-spectral camera system, a camera control system, and a radiometer for normalizing images. To overcome the difficulties currently associated with correlating imagery data with what is actually occurring on the ground (a process known as ground truthing); a hyperspectral reflect... Y. Lan, Y. Huang, D.E. Martin, W.C. Hoffmann, B.K. Fritz, J.D. López |
5. Precision Farming by Means of Remote Sensing.In order to improve the wine quality a study has been carried out on a vineyard. From two different types of satellite images, 5 products have been obtained and represented in maps. DMC-UK images, with a resolution of 32 meters and QUICK-BIRD images, with a resolution of 0.6 meters have been used. Through the bands of these images, the following products were obtained: the NDVI, with which users find out which zones in their estates have the worst condition; Mean Vegetation State, which is a ... J.L. Casanova, S. Fraile, A. Romo, J. Sanz, C. Moclán |
6. Remote Sensing-based Biomass Maps for an Efficient Use of FertilizersFor decades the main objective of farmers was to get the highest yields from their farmland. Nowadays, quality of agricultural products is becoming more and more important for the largest returns. In addition, the effects on our environment are also becoming important. These put increasing limitations on modern agriculture. So-called site-specific management can optimize the input of, for instance, nutrients and pesticides to the need of the plants. In this study, the objective was to study w... J.G. P.w clevers, K.H. Wijnholds, J.N. Jukema |
7. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching pa... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba |
8. Crop Water Stress Mapping for Site Specific Irrigation by Thermal Imagery and Artificial Reference SurfacesVariable rate irrigation machines or solid set systems have become technically feasible; however, crop water status mapping is necessary as a blueprint to match irrigation quantities to site-specific crop water demands. Remote thermal sensing can provide these maps in sufficient detail and at a timely delivery. In a set of aerial and ground scans at the Hula Valley, Israel, digital crop water stress maps were generated using geo-referenced high- resolution thermal imagery and artificial refer... M. Meron, J. Tsipris, V. Orlov, V. Alchnatis, Y. Cohen |
9. Using Drone Based Sensors to Direct Variable-Rate, In-Season, Aerial Nitrogen Application on CornImproving nutrient management on farms is a critical issue nationwide. Applying a portion of N fertilizer during the growing season, alongside the growing corn crop is one way to improve nitrogen management. Sidedress N applications allow the availability of N fertilizer to more closely match the time when the crop is rapidly uptaking N. Additionally, waiting to apply a portion of the N during the growing season allows for management which is responsive to current growing season conditions.... L.J. Thompson |
10. Utilization of Spatially Precise Measurements to Autocalibrate the EPIC Agroecosystem ModelCorn nitrogen recommendations for individual fields must improve to minimize the negative influence that agriculture has on the environment and society. Two adaptive N management approaches for making in-season N fertilizer recommendations are remote sensing and crop systems modeling. Remote sensing has the advantage of characterizing the spatial variability at a high spatial resolution, and crop models are prognostic and can assess expected additions and losses that are not yet reflected by ... T. Nigon, D. Mulla, C. Yang |
11. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen RatesNitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account fo... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf |
12. Active Canopy Sensors for the Detection of Non-Responsive Areas to Nitrogen Application in WheatActive canopy sensors offer accurate measurements of crop growth status that have been used in real time to estimate nitrogen (N) requirements. NDVI can be used to determine the absolute amount of fertilizer requirement, or simply to distribute within the field an average rate defined by decision models using other diagnostics. The objective of this work was to evaluate the capacity of active canopy sensors to determine yield and N application requirements within a site at jointing stage (Fee... A.G. Berger, E. Hoffman, N. Fassana, F. Alfonso |
13. Using a UAV-Based Active Canopy Sensor to Estimate Rice Nitrogen StatusActive canopy sensors have been widely used in the studies of crop nitrogen (N) estimation as its suitability for different environmental conditions. Unmanned aerial vehicle (UAV) is a low-cost remote sensing platform for its great flexibility compared to traditional ways of remote sensing. UAV-based active canopy sensor is expected to take the advantages of both sides. The objective of this study is to determine whether UAV-based active canopy sensor has potential for monitoring rice N statu... S. Li, Q. Cao, X. Liu, Y. Tian, Y. Zhu |
14. Deriving Fertiliser VRA Calibration Based on Ground Sensing Data from Specific Field ExperimentsNitrogen (N) fertilisation affects both rice yield and quality. In order to improve grain yield while limiting N losses, providing N fertilisers during the critical growth stages is essential. NDRE is considered a reliable crop N status indicator, suitable to drive topdressing N fertilisation in rice. A multi-year experiment on different rice varieties (Gladio, Centauro, and Carnaroli) was conducted between 2011 and 2017 in Castello d’Agogna (PV), northwest Italy, with the aim of i) est... E. Cordero, D. Sacco, B. Moretti, E.F. Miniotti, D. Tenni, G. Beltarre, M. Romani, C. Grignani |
15. Active and Passive Sensor Comparison for Variable Rate Nitrogen Determination and Accuracy in Irrigated CornThe objectives of this research were to (i) compare active and passive crop canopy sensors’ sidedress variable rate nitrogen (VRN) derived from different vegetation indices (VI) and (ii) assess VRN recommendation accuracy of active and passive sensors as compared to the agronomic optimum N rate (AONR) in irrigated corn. This study is comprised of six site-years (SY), conducted in 2015, 2016 and 2017 on different soil types (silt loam, loam and sandy loam) and with a range of preplant-ap... L. Bastos, R.B. Ferguson |
16. Use of Field Diagnostic Tools for Top Dressing Nitrogen Recommendation When Organic Manures Are Applied in Humid Mediterranean ConditionsNitrogen is often applied in excessive quantities, causing nitrogen losses. In recent years, the management of large quantities of manure and slurry compounds has become a challenge. The aim of this study was to assess the usefulness of the proxy tools Yara N-testerTMand RapidScan CS-45 for diagnosing the N nutritional status of wheat crops when farmyard manures were applied. Our second objective was to start designing a N fertilization strategy based on these measurements. To achieve these o... A. Castellón, A. Aizpurua, M. Aranguren |
17. Predicted Nitrate-N Loads for Fall, Spring, and VRN Fertilizer Application in Southern MinnesotaNitrate-N from agricultural fields is a source of pollution to fresh and marine waters via subsurface tile drainage. Sensor-based technologies that allow for in-season monitoring of crop nitrogen requirements may represent a way to reduce nitrate-N loadings to surface waters by allowing for fertilizer application on a more precise spatial and temporal resolution. However, little research has been done to determine its effectiveness in reducing nitrate-N losses. In this study... G.L. Wilson, D.J. Mulla, J. Galzki, A. Laacouri, J. Vetsch |
18. Improving Corn Nitrogen Rate Recommendations Through Tool FusionImproving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation ... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer |
19. Utilizing Weather, Soil, and Plant Condition for Predicting Corn Yield and Nitrogen Fertilizer ResponseImproving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three gr... N.R. Kitchen, M.A. Yost, C.J. Ransom, G. Bean, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer |
20. Levels of Inclusion of Crambe Meal (Crambe Abyssinica Hochst) in Sheep Diet on the Balance of Nitrogen and Ureic Nitrogen in the Blood SerumCrambe meal, which is a co-product of biodiesel production, is a potential substitute for conventional protein sources in ruminant diets. The objective of this study was to evaluate the effect of the substitution of crude protein of the concentrate by crude protein of crambe meal with increasing levels (0, 25, 50, and 75%) on nitrogen balance and blood plasma urea nitrogen concentration in sheep. Four male sheep, rumen fistulated, were placed in metabolic crates and distributed in a 4 x 4 Lat... K.K. De azevedo, D.M. Figueiredo, M.G. De sousa, G.M. Dallago, R.R. Silveira, L.D. Da silva, L.N. Rennó, R.A. Santos |
21. Evaluating Remote Sensing Based Adaptive Nitrogen Management for Potato ProductionConventional nitrogen (N) management for potato production in the Upper Midwest, USA relies on using split-applications of N fertilizer or a controlled release N product. Using remote sensing to adaptively manage N applications has the potential to improve N use efficiency and reduce losses of nitrate to groundwater, which are important regional concerns. A two-year plot-scale experiment was established to evaluate adaptive N-management using remote sensing compared to conventional practices ... B. Bohman, D. Mulla, C. Rosen |
22. Improving Active Canopy Sensor-Based In-Season N Recommendation Using Plant Height Information for Rain-Fed Maize in Northeast ChinaThe inefficient utilization of nitrogen (N) fertilizer due to leaching, volatilization and denitrification has resulted in environmental pollution in rain-fed maize production in Northeast China. Active canopy sensor-based in-season N application has been proven effective to meet maize N requirement in space and time. The objective of this research was to evaluate the feasibility of using active canopy sensor for guiding in in-season N fertilizer recommendation for rain-fed maize in Northeast... X. Wang, Y. Miao, T. Xia, R. Dong, G. Mi, D.J. Mulla |
23. Precision Nitrogen and Water Management for Enhancing Efficiency and Productivity in Irrigated MaizeNitrogen and water continue to be the most limiting factors for profitable maize production in the western Great Plains. The objective of this research was to determine the most productive and efficient nitrogen and water management strategies for irrigated maize. This study was conducted in 2016 at Colorado State University’s Agricultural Research Development and Educational Center, in Fort Collins, Colorado. The experiment included a completely randomized block design with ... E. Phillippi, R. Khosla, L. Longchamps, P. Turk |
24. Practical Prescription of Variable Rate Fertilization Maps Using Remote Sensing Based Yield PotentialThis paper describes a practical approach for the prescription of variable rate fertilization maps using remote sensing data (RS) based on satellite platforms, Landsat 8 and Sentinel-2 constellation. The methodology has been developed and evaluated in Albacete, Spain, in the framework of the project FATIMA (http://fatima-h2020.eu/). The global approach considers the prescription of N management prior to the growing season, based on a spatially distributed N balance. Although the diagnosis of ... A. Osann, I. Campos, M. Calera, C. Plaza, V. Bodas, A. Calera, J. Villodre, J. Campoy, S. Sanchez, N. Jimenez, H. Lopez |
25. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing DataThis study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the succ... D. Li, H. Jiang, S. Chen, C. Wang |
26. Experimental Study Using Wind Tunnel for Measuring Variability of Spray Drift SedimentationSpray drift is defined as physical movement of pesticides by air action as a particle droplet and is not deposited on the intended target. Evaluation of the parameters affecting on spray drift is difficult. The accurate studies are expensive, as well as, the variability is high under field conditions due to instability in wind speed and turbulence. Wind tunnel experiments are adequate to simulate the results of field measurements for spray drift. A laboratory experiments were carried out to s... M.H. Alheidary, J. Douzals, C. Sinfort |
27. Rapid Identification of Mulberry Leaf Pests Based on Near Infrared Hyperspectral ImagingAs one of the most common mulberry pests, Diaphania pyloalis Walker (Lepidoptera: Pyralididae) has occurred and damaged in the main sericulture areas of China. Naked eye observation, the most dominating method identifying the damage of Diaphania pyloalis, is time-wasting and labor consuming. In order to improve the identification and diagnosis efficiency and avoid the massive outbreak of Diaphania pyloalis, near infrared (NIR) hyperspectral imaging technology combined with partial least discr... L. Yang, L. Huang, L. Meng, J. Wang, D. Wu, X. Fu, S. Li |
28. Optimum Spatial Resolution for Precision Weed ManagementThe occurrence and number of herbicide-resistant weeds in the world has increased in recent years. Controlling these weeds becomes more difficult and raises production costs. Precision spraying technologies have been developed to overcome this challenge. However, these systems still have relatively high acquisition cost, requiring studies of the relation between the spatial distribution of weeds and the economically optimum spatial resolution of the control method. In this context, the object... R.G. Trevisan, M.T. Eitelwein, M.N. Ferraz, T.R. Tavares, J.P. Molin, D.C. Neves |
29. Real-Time Control of Spray Drop ApplicationElectrostatic application of spray drops provides unique opportunities to precisely control the application of pesticides due to the additional electrostatic force on the spray drops, in addition to the normally seen forces of aerodynamic drag, gravity, and inertia. In this work, we develop a computational model to predict the spray drop trajectories. The model is validated through experiments with high speed photography of spray drop trajectories, and quantification of which trajectories lea... S. Post, M. Jermy, P. Gaynor, N. Kabaliuk, A. Werner |
30. Spatial Variability of Optimized Herbicide Mixtures and DosagesDriven by 25 years of Danish, political 'pesticide action plans', aiming at reducing the use of pesticides, a Danish Decision Support System (DSS) for Integrated Weed Management (IWM) has been constructed. This online tool, called ‘IPMwise’ is now in its 4th generation. It integrates the 8 general IPM-principles as defined by the EU. In Denmark, this DSS includes 30 crops, 105 weeds and full assortments of herbicides. Due to generic qualities in both the integrat... P. Rydahl, R.N. Jorgensen, M. Dyrmann, N. Jensen, M.D. Sorensen, O.M. Bojer, P. Andersen |
31. Detecting Basal Stem Rot (BSR) Disease at Oil Palm Tree Using Thermal Imaging TechniqueBasal stem rot (BSR), caused by Ganoderma boninense is known as the most damaging disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the cost of plantation management. This study focuses on identifying the thermal properties of healthy and BSR-infected tree using a thermal ima... S. Bejo, G. Abdol lajis, S. Abd aziz, I. Abu seman, T. Ahamed |
32. On-Farm Experimentation and Decision-Support WorkshopThis 3-hour workshop discusses the requirements, methods and theories that may be used to assist in making optimal crop management decisions. The first part will focus on on-farm experimentation (OFE): 1) organization and benefits of OFE; 2) social processes and engagement; 3) designs, data and statistics. The second part will demonstrate how to generate insights applicable at the individual farm level using results from research trials collected in a diversity of contexts. Data sharing, meta... S. Cook, M. Lacoste, F. Evans, N. Tremblay, V. Adamchuk |
33. Application of Drone Data to Assess Damage Intensity of Bacterial Leaf Blight Disease on Rice Crop in IndonesiaThe Government of Indonesia has launched agricultural insurance program since 2016. A key in agricultural insurance is damage assessment which is required to be as precise, quick, quantitative and inexpensive as possible. Current method is to inspect the damage by human eyes of specialist having experiences. This method, however, costs much and is difficult to estimate disease infected fields precisely in wide area. So, there is increasing need to develop effective, simplified and low cost me... C. Hongo, S. Isono, G. Sigit, B. Utoyo, E. Tamura |
34. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS ImageryAerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimati... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |
35. Knowledge-based Approach for Weed Detection Using RGB ImageryA workflow was developed to explore the potential use of Phase One RGB for weed mapping in a herbicide efficacy trial in wheat. Images with spatial resolution of 0.8 mm were collected in July 2020 over an area of nearly 2000 square meters (66 plots). The study site was on a research farm at the University of Saskatchewan, Canada. Wheat was seeded on June 29, 2020, at a rate of 75 seeds per square meter with a row spacing of 30.5 cm. The weed species seeded in the trial were kochia, wild oat, ... T. Ha, K. Aldridge, E. Johnson, S.J. Shirtliffe, S. Ryu |
36. UAV-based Hyperspectral Monitoring of Peach Trees As Affected by Silicon Applications and Water Stress StatusPrevious research has shown that the application of reduced doses of Silicon (Si) improves crop tolerance to water stress, which is common in commercial young peach trees because irrigation is not usually applied during their first two years. In this study, aerial images were used to monitor the impact of different Si and water treatments on the hyperspectral response of peach trees. An experiment with 60 young (under 1 year old) peach trees located at the Musser Fruit Research Center (Seneca... J. Peña, J. Melgar, A. De castro, J. Maja, K. Nascimento-silva |
37. N-management Using Structural Data: UAV-derived Crop Height As an Estimator for Biomass, N Concentration, and N Uptake in Winter WheatIn the last 15 years, sensors mounted on Unmanned Aerial Vehicles (UAVs) have been intensively investigated for crop monitoring. Besides known remote sensing approaches based on multispectral and hyperspectral sensors, photogrammetric methods became very important. Structure for Motion (SfM) and Multiview Stereopsis (MVS) analysis approaches enable the quantitative determination of absolute crop height and crop growth. Since the first paper on UAV-derived crop height was published by Bendig e... G. Bareth, A. Jenal, H. Hüging |
38. Cotton Boll Detection and Yield Estimation Using UAS Lidar Data and RGB ImageCotton boll distribution is a critical phenotypic trait that represents the plant's response to its environment. Accurate quantification of boll distribution provides valuable information for breeding cultivars with high yield and fiber quality. Manual methods for boll mapping are time-consuming and labor-intensive. We evaluated the application of Lidar point cloud and RGB image data in boll detection and distribution and yield estimation. Lidar data was acquired at 15 m using a DJI Matri... Z. Lin, W. Guo, N. Gill |
39. Integration of Unmanned Aerial Systems Images and Yield Monitor in Improving Cotton Yield EstimationThe yield monitor is one of the most adopted precision agriculture technologies because it generates dense yield data to quantify the spatial variability of crop yield as a basis for site-specific management. However, yield monitor data has various errors that prevent proper interpretation and precise field management. The objective of this study was to evaluate the application of unmanned aerial systems (UAS) images in improving cotton yield monitor data. The study was conducted in a dryland... H. Gu, W. Guo |
40. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS ImageryDeep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high re... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
41. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen ContentEstimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acqu... R. Karn, H. Gu, O. Adedeji, W. Guo |
42. Establishment of a Canola Emergence Assessment Methodology Using Image-based Plant Count and Ground Cover AnalysisManual assessment of emergence is a time-consuming practice that must occur within a short time-frame of the emergence stage in canola (Brassica napus). Unmanned aerial vehicles (UAV) may allow for a more thorough assessment of canola emergence by covering a wider scope of the field and in a more timely manner than in-person evaluations. This research aims to calibrate the relationship between emerging plant population count and the ground cover. The field trial took place at the Uni... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness |
43. Utilization of UASs to Predict Sugarcane Yields in Louisiana Prior to HarvestOne of the most difficult tasks that both sugarcane producers and processors face every year is estimating the yields of sugarcane fields prior to the start of harvest. This information is needed by processors to determine when the harvest season is to be initiated each year and by producers to decide when each field should be harvested. This is particularly important in Louisiana because the end of the harvest season is often affected by freeze events. These events can severely damage the cr... R.M. Johnson, B. Ramachandran |
44. Increasing the Accuracy of UAV-Based Remote Sensing Data for Strawberry Nitrogen and Water Stress DetectionThis paper presents the methods to increase the accuracy of unmanned aerial vehicles (UAV)-based remote sensing data for the determination of plant nitrogen and water stresses with increased accuracy. As the demand for agricultural products is significantly increasing to keep up with the growing population, it is important to investigate methods to reduce the use of water and chemicals for water conservation, reduction in the production cost, and reduction in environmental impact. UAV-based r... S. Bhandari, A. Raheja |
45. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote SensingSatellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images ... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo |
46. Enhancing Spatial Resolution of Maize Grain Yield DataGrain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops. Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used t... J. Siegfried, R. Khosla, D. Mandal, W. Yilma |
47. Assessment of Goss Wilt Disease Severity Using Machine Learning Techniques Coupled with UAV ImageryGoss Wilt has become a common disease in corn fields in North Dakota. It has been one of the most yield-limiting diseases, causing losses of up to 50%. The current method to identify the disease is through visual inspection of the field, which is inefficient, and can be subjective, with misleading results, due to evaluator fatigue. Therefore, developing a reliable, accurate, and automated tool for assessing the severity of Goss's Wilt disease has become a top priority. The use of un... A. Das, P. Flores, Z. Zhang , A. Friskop, J. Mathew |
48. Precision Nitrogen and Water Management for Optimized Sugar Beet Yield and Sugar ContentSugar beet (SB) production profitability is based on maximizing three parameters: beet yield, sucrose content, and sucrose recovery efficiency. Efficient nitrogen (N) and water management are key for successful SB production. Nitrogen deficits in the soil can reduce root and sugar yield. Overapplication of N can reduce sucrose content and increase nitrate impurities which lowers sucrose recovery. Application of N in excess of SB crop need leads to vigorous canopy growth, while compromising ro... O.S. Walsh, S. Shafian |
49. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of CottonThe use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationship... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash |
50. Multispectral Assessment of Chickpea in the Northern Great PlainsChickpea is an increasingly important crop in the Montana agricultural system. From 2017 to 2021 the U.S. has planted an average of about 492,000 acres per year with Montana chickpea production accounting for around 44% of the U.S. total (USDA/NASS QuickStats accessed on 2/11/2021). This has led to an increase in breeding efforts for elite varieties adapted to the unique conditions in the Northern Great Plains. Breeding of chickpea often relies on traditional phenotyping techniques that are l... J.M. Vetch |
51. Evaluation of Indwelling Rumen Temperature Monitoring System for Dairy Calf Illness Detection and ManagementPrecision Dairy Farming technology has mostly focused on tools to improve cow care, but new tools are available to improve the care of pre-wean calves and heifers. These technologies apply real-time monitoring to measure individual animal data and detect a deviation from normal. On-farm validation of new technologies remains important for successful deployment of new technologies within commercial farms to understand how the technology can improve dairy calf welfare, performance, and health. ... J.M. Hartschuh, J.P. Fulton, S.A. Shearer, B.D. Enger, G.M. Schuenemann |