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1. Using Electronic Technology to Remotely Monitor Conditions, Transfer the Data, and Display Data Real-time on the InternetThis session describes the use of electronic equipment to monitor soil temperature and moisture, air temperature, relative humidity, wind speed, solar radiation, leaf wetness, and rainfall. Presenter will explain how to use the equipment to monitor conditions, transfer the data, and display the information in real-time on the I... R. Ashley, J. Nowatzki |
2. A Model to Analyze “As-Applied” Reports of Variable Rate ApplicationsVariable rate technology enables users to access crop inputs such as fertilizers and pesticides, based on site specific information. This technology combines a variable rate control system, positioning system and GIS software to enable variable rate application. During operation some of these systems report information (“as-applied” files) about target rates and actual applied rates on georeferenced points along the ... A.F. Colaço, H.J. Rosa, J.P. Molin |
3. Ontology for Data Representation in the Production of Cotton Fiber in Brazil... C.S. Junior, A.R. Hirakawa |
4. Towards a Multi-Source Record Keeping System for Agricultural Product TraceabilityAgricultural production record keeping is the basis of traceability system. To resolve the problem including single method of information acquisition, weak ability of real-time monitoring and low credibility of history information in agricultural production process, t... C. Sun, Z. Ji, J. Qian, M. Li, L. Zhao, W. Li, C. Zhou, X. Du, J. Xie, T. Wu, L. Qu, L. Hao, X. Yang |
5. Issues in Analysis of Soil-Landscape Effects in a Large Regional Yield Map CollectionYield maps are commonly collected by producers and precision-agriculture service providers and are accumulating in warehouse scale data-stores. A key goal in analysis of yield maps is to understand how climate interacts with soil landscapes to cause spatial and temporal variability in grain yield. However, there are many issues that limit utilization of yield map data for this purpose including: i) yield-landscape inversion between climate yea... N.R. Kitchen, K.A. Sudduth, D.B. Myers |
6. Aggregating Precision Agriculture Data Across RegionsThe analysis of precision agricultural data has largely focused on one field at a time and to a lesser extent to one individual farm. Recent developments have allowed those with access to data from across large regions to realize additional value by pooled community analysis of precision agriculture data. Pool data analysis has provided greater value to individual farms than they would have gained by only using their own farm-level data. Statistical, economic, and risk methodologie... T. Griffin |
7. Spectral Discrimination Of Early Dchinochloa Crasgalli And Echinochloa Crusgalli In Corn And Soybean By Using Support Vector MachinesThe key to realize precise chemical application is weed identification. This paper introduces a kind of multi-classification mode based on Support Vector Machines (SVM) and one-against-one-algorithm for weed seedlings (Dchinochloa crasgalli, and Echinochloa crusgalli) in corn and soybean fields. A handheld FieldSpec® 3 Spectroradiometer manufactured by ASD Inc., in USA was used to measure the spectroscopic data of the canopies of the seedlings of corn, soy... W. Deng, G. Wu |
8. A Comparison Of Conventional And Sensor-based Lime Requirement MapsSuccessful variable-rate applications of agricultural inputs, such as lime, rely on quality of input data. Systematic soil sampling is... A.K. Jonjak, V.I. Adamchuk, C.S. Wortmann, C.A. Shapiro, R.B. Fergugson |
9. Development Of A System For Site-specific Nematicide Placement In CottonNematode distribution varies significantly in cotton fields. Population density throughout a field is highly correlated to soil texture. Field-wide application of a uniform nematicide rate results in the chemical being applied to areas without nematodes or where nematode densities are below an economic threshold, or the application of sub-effective levels in areas with high nematode densities. The investigators have developed a “Site- Specific Nematicide Placement”... A. Khalilian, W. Henderson, J. Mueller, T. Kirkpatrick, S. Monfort, C. Overstreet |
10. A Clustering Approach For Management Zone Delineation In Precision AgricultureIn recent years, an increasing amount of research has been devoted to the delineation of management zones. There have been quite a number of approaches towards using small-scale data for subdividing the field into a small number of zones, usually three or four. However, these zones are usually static, often require multi-year data sets and are based on low-resolution sampling methods for data acquisition. Furthermore, existing research into th... G. Ru, M. Schneider, R. Kruse |
11. On The Go Soil Sensor For Soil Ec MappingThis paper describes spatial variation maps of soil electrical conductivity (EC) obtained by both spectroscopic and capacitance methods using on the go soil sensor ( a real-time soil sensor -RTSS) SAS 1000, commercialized by Shibuya Kogyo Co. The experiments were conducted over a 2 year period on an experimental Hokkaido farm with an alluvial soil type. The comparison in soil EC records between the spectroscopy and the capacitance were also discussed. The spectroscopic approach used the soi... N. Sulastri, S. Shibusawa, M. Kodaira |
12. Using Multiplex® And GreenseekerTM To Manage Spatial Variation Of Vine Vigor In ChampagneSébastien Debuisson1, Marine Le Moigne2, Mathieu Grelier1, Sébastien Evain2, Laurent Panigai1, Zoran G. Cerovic3 1CIVC, 5 rue Henri-Martin, boîte postale 135, Epernay, France 2Force-A, Université Paris Sud, Bât 503, Orsa... S. Debuisson, L. Marine |
13. Spatial Mapping Of Penetrometer Resistance On Turfgrass Soils For Site-specific CultivationSite-specific management requires site-specific information. Soil compaction at field capacity is a major stress on recreational turfgrass sites that requires frequent cultivation. Spatial mapping of penet... K. Rice, T. Carson, J. Krum, I. Flitcroft, V. Cline, R. Carrow |
14. Nitrogen Loss In Corn Production Varies As A Function Of Topsoil DepthUnderstanding availability and loss potential of nitrogen for varying topsoil depths of poorly-drained claypan soil landscapes could help producers make improve decisions when managing crops for feed grain or bio-fuels. While it has been well documented that topsoil depth on these soils plays an important role in storing water for crop growth, it is not well known how this same soil... E. Allphin, N.R. Kitchen, K.A. Suddeth, A. Thompson |
15. The Soil P2O5 Mapping Using The Real Time Soil SensorMany researches related to P2O5 measurement using Vis-NIR spectroscopy have been performed in laboratory. There are not so many researches to perform on-the-go measurement of P2O5. One of the researches which performe... M. Kodaira, Y. Nagami, S. Shibusawa, R. Kanda |
16. Spatial Variability Analyse And Correlation Between Physical Chemical Soil Attributes And Sugarcane Quality ParametersWith the high increment in the ethanol demand, the trend is that the planted area with sugar cane in Brazil will increase from the actual 7 million ha up to 12 million ha in 15 years. The sugar cane expansion demands, beyond the enlargement of the boundaries with the installation of new industrial units, better use of the production areas and improvement of the yield and quality, together with production costs reduction. In such a way, the adoption of Precision Ag... F. Rodrigues jr, P.S. Maglh, D.G. Cerri |
17. Dozen Parameters Soil Mapping Using The Real-time Soil SensorA Real-time soil sensor (RTSS) can be predicted soil parameters using near-infrared underground soil reflectance sensor in commercial farms. ... M. Kodaira, S. Shibusawa, K. Ninomiya |
18. Spatial Variability Of Measured Soil Properties Across Site- Specific Management ZonesThe spatial variation of productivity across farm fields can be classified by delineating site-specific management zones. Since productivity is influenced by soil characteristics, the spatial pattern of productivity could be caused by a corresponding variation in certain soil properties. Determining the source of variation in productivity can help achieve more effective site-specific management, the objectives of this study were (i) to characterize the spatial variability of soil physical pro... M. Mzuku, R. Khosla, R. Reich, G. Http://icons.paqinteractive.com/16x16/ac, F. Smith, L. Macdonald |
19. Spatial-temporal Management Zones For Biomass MoistureBiomass handling operations (harvesting, raking, collection, and transportation) are critical operations within the agricultural production system since they constitute the first link in the biomass supply chain, a fact of substantial importance considering the increasingly involvement of biomass in bio-refinery and bio-energy procedures. Nevertheless, the inherent uncertainty, imposed by the interaction between environmental, biological, and machinery factors, makes the available sched... S. Fountas, D. Bochtis, C. Sorensen, O. Green, R. J, T. Bartzanas |
20. Interaction Between Air Spray Drift And Climatic Conditions Creating Drift Map Related To The Aerial Application Of Pesticides Using Low Volumes In BrazilBetween 30 to 50% of the pesticides total applied over agricultural areas can be lost by the air, depending of the applying conditions, by the spray drift action. This spray drift problem is increased when the field is too close to the urban locations, bringing environmental contamination, and when the application is made with oil on the tank mixture. The society demands ... F. Baio, U. Antuniassi |
21. A Case Study For Variable-rate Seeding Of Corn And Cotton In The Tennessee Valley Of AlabamaFarmers have recently become more interested in implementing variable-rate seeding of corn and cotton in Alabama due to increasing seed costs and the potential to maximize yields site-specifically due to inherent field variability. Therefore, an on-farm case study was conducted to evaluate the feasibility of variable-rate seeding for a corn and cotton rotation.... S.H. Norwood, J.P. Fulton, A.T. Winstead, J.N. Shaw, D. Rodekohr, C.J. Brodbeck, T. Macy |
22. Estimating Soil Moisture And Organic Matter Content Variabality Using Electromagnatic Induction MetodAbstract: Electromagnetic induction (EMI) methods are gaining popularity due to their non-destructive nature, rapid response and ease of integration into mobile platforms for assessment of the soil moisture content, water table depth, and salinity etc. The objective of this study was to estimate and map soil moisture content and organic matter content using Dua... A. Farooque, Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, T. Stauffer |
23. Assessment Of The Success Of Variable Rate Seeding Based On EMI MapsGood plant establishment is the critical first step in growing a crop. To achieve this, the correct seed rate must be calculate. This is done by assessing the optimum target plant population per m² and then making an estimate of any losses over winter. Losses will depend on the quality of seedbed created which is related to texture, stoniness and compaction of the soil. If there is any variation in these field characteristics then the correct see... S. Griffin, M. Darr |
24. Spatio-temporal Analysis Of Atrazine Degradation And Associated Attributes In Eastern Colorado SoilsAtrazine catabolism is an example of a rapidly evolved soil microbial adaptation. In the last 20 years, atrazine-degrading bacteria have become globally distributed, and many soils have developed enhanced capacities to degrade atrazine, reducing its half-life from 60 to a few days or less. While the presence of atrazine-degrading bacteria determine a soil's potential to catabolize at... M. Stromberger, R. Khosla, D. Shaner, D. Zach |
25. Validation Of On-the-go Soil Ph-measurements – Primary Results From GermanyUntil recently in-field variability for soil pH could not be considered for agronomic decisions (e.g. liming rates) because reliable spatial information was hardly available. The required density of soil pH-measurements could not be achieved by manual soil sampling due to time constraints and analysis costs for the vast number of samples. A compreh... H. Olfs, D. Trautz, A. Borchert |
26. Carbohydrate Reserves On Tapping Systems And Production Of Hevea BrasiliensisCARBOHYDRATE RESERVES ON TAPPING SYSTEMS AND PRODUCTION OF Hevea brasiliensis Chantuma P1., Lacointe A2., Kasempsap P3., Thanysawanyangkura S4., Gohet E5., Clément A6., Guilliot A7., Améglio T2., Thaler P8. and Chantuma A1. 1 Agriculture Scientist Senior, Chachoengsao Rubber Research Center, RRIT-DOA, Ministry of Agriculture and Cooperative, Sanam Chai Ket, Thailand. 2 INRA, UMR 547 PIAF, F-60100 Clermont-Ferrand, France. 3 Departmen... D. Chantuma, M. Zaller |
27. Spatial Variability Of Important Soil Characteristics In Semiarid Ecosystems, A Case Study In Arsanjan Plain, Southern IranTimely information on the content and distribution of key soil nutrients in highly calcareous ecosystems is vital to support precision agriculture. Efficient tools to measure within-field spatial variation in soil are important when establishing agricultural field trials and in precision farming. Therefore, soil samples were collected at 0-30 cm depth in highly calcareous soils (Arsanjan plain) and chemically analyzed for nitrate (NO3-), e... M.P. Baghernejad, M.M. Emadi |
28. Does Pasture Longevity Under Direct Grazing Affect Field-scale Sorghum Yield Spatial Variability In Crop-pasture Rotation Systems?Crop yield spatial variability is usually related to terrain attributes and soil properties. In pasture systems, soil properties are affected by animal grazing. However, soil and terrain attributes relation with crop yield variability has not been assessed in crop-pasture rotat... V. Pravia, J.A. Terra, Roel |
29. Application Of A Canopy MultisensorThe MobilLas mobile canopy sensor was initially developed for variable rate fertilisation and plant protection. Because of the several canopy variables sensed the sensor has wider application in crop and soil variability studies, detailed crop water balance studies, spatial modelling of p... A. Thomsen, K. Schelde |
30. Site-specific Phosphorus And Potassium Fertilization Of Alfalfa: Fertilizer Usage And Sampling Density ComparisonAlfalfa accounts for the largest cropping area in both the High Desert and Intermountain regions in California, and the use of site-specific management (SSM) can potentially improve farmers’ fertilization practices and crop nutritional status. These areas have limited to no studies regarding nutrient SSM, and variable rate (VR) fertilizer application has not been commonly used by farmers in either area. Considerable range of soil nutrient levels have... A. Biscaro, S. Orloff |
31. Impact Of Winter Grazing On Forage Biomass Topography Soil Strength Spatial RelationshipsSpatial relationships between soil properties, forage productivity, and landscape can be used to manage site-specific grazing. Soil penetration resistance and forage biomass were collected for three years in winter grazing experiment. The three ha experimental area was divided into six paddocks, hay was cut twice per year in the months of May and June, and forage stockpiled after the second cutting. Animals were admitted to paddocks at the end of November, at a stocking r... E.M. Pena-yewtukhiw, D. Mata-padrino, W. Bryan |
32. Spatial Variability Of Spikelet Sterility In Temperate Rice In ChileSpikelet sterility (blanking) causes large economic losses to rice farmers in Chile. The most common varieties are susceptible to low air and water temperatures during pollen formation and flowering, which is the main responsible for the large year to year variation observed in terms of blanking and, therefore, of grain yield. The present work had for objective to study the spatial variability of spikelet sterility within two rice fields, during two consecutive seasons, and relate it to water... R.A. Ortega, D.E. Del solar, E. Acevedo |
33. Spatial And Temporal Changes In Atrazine Degradation Rates In SoilAtrazine is a widely used soil-applied herbicide to control many broadleaf and grassy weeds in corn, sugarcane, and non-cropland areas. Atrazine is also found as a contaminant in surface and ground water. One of the strengths and weaknesses of atrazine has been the long residual activity in the soil that provides good weed control but also increases the leaching of the herbicide. In the las... D. Shaner |
34. Measuring Multi-depth Soil Moisture Content In A Vertisol Soils With EM38Over the years, electromagnetic induction sensors, such as EM38, have been used to monitor soil salinity or local electrical conductivity (ECa) and their output has been instrumented in establishing models for depth profiling of ECa. In the previous work both the forward propagation and inverse matrix approaches offered potential to produce depth profiles of soil ECa. However, it remains a question whether EM38 is able to measure v in different depths. This present study concerns itse... B. Hossain |
35. Spatial Variation Patterns Of Soil Properties And Winter Wheat Growth Parameters In China National Experiment Station For Precison AgricultureUnderstanding of spatial patterns of soil properties and crop growth and their relationship is neccesary for variable-rate management of farmland in precision agriculture. This paper presents spatial variation patterns of soil properties such as depth of soil diagnostic horizons, cation exchange capacity, organic matter content, soil solution nutrients concentration, and winter wheat growth and yield parameters in China National Experiment Station for Precison A... X. Xue, L. Chen |
36. Suitability Of Fluorescence Sensors To Estimate The Susceptibility Degree Of Spring Barley To Powdery Mildew And Leaf RustThe overall role of precision agriculture is not restricted to those systems for in-field and in-season sensing of the impact of stresses. Much more, its contribution comprises the prevention of stresses, amongst others by supporting the selection of appropriate and stress-tolerant genotypes in breeding programs. In this context, the development, selection and use of cultivars which are tolerant to pathogens establish an essential tool for a more sustainable and environmental-fr... G. Leufen, G. Noga, M. Hunsche |
37. Statistical Variability of Crop Yield, Soil Test N and P Within and Between Producer’s FieldsSoil test N and P significantly affect crop production in the Canadian Prairies, but vary considerably within and between producer's fields. This study describes the variability of crop yield, soil test N and P within and between producer's fields in the context of variable fertilizer rates. Yield, terrain attribute, soil test N and P data were collected for 10 fields in Alberta, Saskatchewan and Manitoba Canada in 2014 and 2015. The influence of ... A. Moulin, M. Khakbazan |
38. Understanding Complex Soil Variability: the Application of Archaeological Knowledge to Precision Agriculture Systems in the UK.As higher resolution datasets have become more available and more accessible within commercial agriculture, there has been an increasing expectation that more data will bring more answers to questions surrounding soil, crop and yield variability. When this does not happen, trust and confidence in data can be lost, affecting the uptake and use of precision agriculture. This research presents a novel approach for understanding complex soil variability at a variety of different scales.... H. Webber |
39. Estimating Environmental Systems Using Iterated Sigma Point Techniques: a Biomass Substrate Hypothetical SystemThis paper addresses the problem of biomass substrate hypothetical system estimation using sigma points kalman filter (SPKF) methods. Various conventional and state-of-theart state estimation methods are compared for the estimation performance, namely the unscented Kalman filter(UKF), the central difference Kalman filter (CDKF), the square-root unscented Kalman filter (SRUKF), the square-root central difference Kalman filter (SRCDKF), the iterated unscented Kalman filter (IUKF), the iterated ... I. Baklouti, M. Mansouri, M. Destain, A. Hamida |
40. Spatial and Temporal Variation of Soil Nitrogen Within Winter Wheat Growth SeasonThis study aims to explore the spatial and temporal variation characteristics of soil ammonium nitrogen and nitrate nitrogen within winter wheat growth season. A nitrogen-rich strip fertilizer experiment with eight different treatments was conducted in 2014. Soil nitrogen samples of 20-30cm depth near wheat root were collected by in-situ Macro Rhizon soil solution collector then soil ammonium nitrogen and nitrate nitrogen content determined by SEAL AutoAnalyzer3 instrument. Classical statisti... X. Song, G. Yang, Y. Ma, R. Wang, C. Yang |
41. Rectification of Management Zones Considering Moda and Median As a Criterion for Reclassification of PixelsManagement zones (MZ) make economically viable the application of precision agriculture techniques by dividing the production areas according to the homogeneity of its productive characteristics. The divisions are conducted through empirical techniques or cluster analysis, and, in some cases, the MZ are difficult to be delimited due to isolated cells or patches within sub-regions. The objective of this study was to apply computational techniques that provide smoothing of MZ, so as to become v... N.M. Betzek, E.G. Souza, C.L. Bazzi, K. Schenatto, A. Gavioli, M.F. Maggi |
42. Positioning Strategy of Maize Hybrids Adjusting Plant Population by Management ZonesChoice of hybrid and accurate amount of plants per area determines grain yield and consequently net incomes. Local field adjustment in plant population is a strategy to manage spatial variability and optimize environmental resources that are not under farmer control (like soil type and water availability). This study aims to evaluate the response of hybrids by levels of plant population across management zones (MZ). Six different hybrids and five rates of plant populations were analyzed start... A.A. Anselmi, J.P. Molin, M.T. Eitelwein, R. Trevisan, A. Colaço |
43. Should One Phosphorus Extraction Method Be Used for VRT Phosphorus Recommendation in the Southern Great Plains?Winter Wheat has been produced throughout the southern Great Plains for over 100 years. In most cases this continuous production of mono-culture lower value wheat crop has led to the neglect of the soils, one such soil property is soil pH. In an area dominated by eroded soils and short term leases, Land-Grant University wheat breeders have created lines of winter wheat which are aluminum tolerant to increase production in low productive soils. Now the fields in this region can hav... D.B. Arnall, S. Phillips, C. Penn, P. Watkins, B. Rutter, J. Warren |
44. Consequences of Spatial Variability in the Field on the Uniformity of Seed Quality in Barley Seed CropsSpatial variation is known to affect cereal growth and yield but consequences for seed quality are less well-known. Intra-field spatial variation occurs in soil and environmental variables and these are expected to affect the crop. The objective of this paper was to identify the spatial variation in barley seed quality and to investigate its association with environmental factors and the spatial scale over which this correlation occurs. Two uniformly-managed, commercial fields of wi... S. Hama rash, A.J. Murdoch |
45. Processing Yield Data from Two or More CombinesErroneous data affect the quality of yield map. Data from combines working close to each other may differ widely if one of the monitors is not properly calibrated and this difference has to be adjusted before generating the map. The objective of this work was to develop a method to correct the yield data when running two or more combines in which at least one has the monitor not properly calibrated. The passes of each combine were initially identified and three methods to correct yield data w... L. Maldaner, J.P. Molin, T.F. Canata |
46. The New Digital Soil Map of Sweden -Derived for Free Use in Precision AgricultureThe Digital Soil Map of Sweden (DSMS) was finalized in 2015. The present paper describes the mapping strategy, the estimated uncertainty of the primary map layers and its potential use in precision agriculture. The DSMS is a geodatabase with information on the topsoil of the arable land in Sweden. The spatial resolution is 50 m × 50 m and it covers > 90% of the arable land of the country (~2.5 million ha). Non-agriculture land and areas with organic soil are excluded. Access to a num... K. Piikki, M. Söderström |
47. Shifting Fertiliser Response Zones in a Four Year, Whole-paddock Cereal Cropping Experiment.Precision agriculture in cropping areas of dryland Australia has focused on managing within production zones. These are ideally stable, possibly soil- and topography-based areas within fields. There are many different ideas on how to delimit and implement zones, and a four year whole-field experiment, with low, medium and high treatment philosophies applied per 9m seeder/harvester width across the entire field, was established to explore how zones might best be established and used. The treat... B. Jones, T. Mcbeath, N. Wilhelm |
48. Spatial Variability of Soil Nutrients and Site Specific Nutrient Management in MaizeA field study was conducted during kharif 2014 and rabi 2014-15 at Southern Transition Zone of Karnataka under the jurisdiction of University of Agricultural Sciences, GKVK, Bangalore, India to know the spatial variability for available nutrient content in cultivator’s field and effect of site specific nutrient management in maize. The farmer’s fields have been delineated with each grid size of 50 m x 50 m using geospatial technology. Soil samples from 0-15 cm we... S. T, M. Giriyappa, D. Hanumanthappa, N. Dr., S. K, S. Yogananda, A. Kiran |
49. Sources of Information to Delineate Management Zones for CottonCotton in Brazil is an input-intensive crop. Due to its cultivation in large fields, the spatial variability takes an important role in the management actions. Yield maps are a prime information to guide site-specific practices including delineation of management zones (MZ), but its adoption still faces big challenges. Other information such as historical satellite imagery or soil electrical conductivity might help delineating MZ as well as predicting crop performance. The objective of this w... R.G. Trevisan, M.T. Eitelwein, A.F. Colaço, J.P. Molin |
50. Measurement of In-field Variability for Active Seeding Depth Applications in Southeastern USProper seeding depth control is essential to optimize row-crop planter performance, and adjustment of planter settings to within field spatial variability is required to maximize crop yield potential. The objectives of this study were to characterize planting depth response to varying soil conditions within fields, and to discuss implementation of active seeding depth technologies in Southeastern US. This study was conducted in 2014 and 2015 in central Alabama for non-irrigated maize (Zea may... A.M. Poncet, J.P. Fulton, T.P. Mcdonald, T. Knappenberger, R.W. Bridges, J. Shaw, K. Balkcom |
51. Response of Soybean Cultivars According to Management Zones in Southern BrazilThe positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three manag... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires |
52. High-resolution Mapping with On-the-go Soil Sensor and Its Relation with Corn Yield and Soil Acidity in a Dystrophic Red OxisolSpatial representations of soil attributes with low resolution can lead to gross errors of recommendation and compromise the efficiency of soil corrections and consequently the grain yield. However, obtaining the spatial variability of soil attributes with high resolution by soil sampling is not recommended because of its large time spent and high cost of laboratory analysis what makes difficult their large-scale application. This way, the on-the-go soil sensing has been used in precision agr... G.M. Corassa, T.J. Amado, R.A. Schwalbert, G.B. reimche, D. Dalla nora, T. . horbe, F.M. tabaldi |
53. Spatial Variability and Correlations Between Soil Attributes and Productivity of Green Corn CropIn Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphi... W.J. Souza, S.H. Benez, P.H. Nakazawa, A.J. Santana neto, L.C. Citon, V.S. Akune |
54. Claypan Depth Effect on Soil Phosphorus and Potassium DynamicsUnderstanding the effects of fertilizer addition and crop removal on long-term change in spatially-variable soil test P (STP) and soil test K (STK) is crucial for maximizing the use of grower inputs on claypan soils. Using apparent electrical conductivity (ECa) to estimate topsoil depth (or depth to claypan, DTC) within fields could help capture the variability and guide site-specific applications of P and K. The objective of this study was to determine if DTC derived from ECa... L. Conway, M. Yost, N. Kitchen, K. Sudduth, B. Myers |
55. In-field Variability of Terrain and Soils in Southeast Kansas: Challenges for Effective ConservationA particular challenge for crop production in southeast Kansas is the shallow topsoil, underlain with a dense, unproductive clay layer. Concerns for topsoil loss have shifted production systems to reduced tillage or conservation management practices. However, historical erosion events and continued nutrient and sediment loss still limit the productive capacity of fields. To improve crop production and further adoption of conservation practices, identification of vulnerable areas of fields was... G.F. Sassenrath, T. Mueller, V.J. Alarcon, S.E. Kulesza, D. Shoup |
56. Field Potential Soil Variability Index to Identify Precision Agriculture OpportunityPrecision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These co... C.W. Bobryk, M. Yost, N. Kitchen |
57. Assessing the Variability of Red Stripe Disease in Louisiana Sugarcane Using Precision Agriculture MethodsSymptoms of red stripe disease caused by Acidovorax avenae subsp. avenae in Louisiana between 1985 and 2010 were limited to the leaf stripe form which caused no apparent yield loss. During 2010, the more severe top rot form was observed, and a study was initiated to investigate the distribution of red stripe in the field and determine its effects on cane and sugar yields. Two fields of cultivar HoCP 00-950, one plant-cane (PC) crop and one first-ratoon (FR) crop, affected by top rot wer... R.M. Johnson, M.P. Grisham |
58. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural EducationThe industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby |
59. Precision Farming Basics Manual - a Comprehensive Updated Textbook for Teaching and Extension EffortsToday precision agricultural technologies are limited by the lack of a workforce that is technology literate, creative, innovative, fully trained in their discipline, able to utilize and interpret information gained from information-age technologies to make smart management decisions, and have the capacity to convert locally collected information into practical solutions. As part of a grant entitled Precision Farming Workforce Development: Standards, Working Groups, and Experimental Lea... K. Shannon |
60. A Content Review of Precision Agriculture Courses Across the USKnowledge of what precision agriculture (PA) content is currently taught across the United States will help build a better understanding for what PA instructors should incorporate into their classes in the future. The University of Missouri partnered with several universities throughout the nation on a USDA challenge grant. Precision Agriculture faculty from 24 colleges/universities from across the U.S. shared their PA content by sharing their syllabi from 43 different courses. The syllabi we... D. Skouby, L. Schumacher, M. Yost, N.R. Kitchen |
61. Knowledge, Skills and Abilities Needed in the Precision Ag Workforce: an Industry SurveyPrecision agriculture encompasses a set of related technologies aimed at better utilization of crop inputs, increasing yield and quality, reducing risks, and enabling information flow throughout the crop supply and end-use chains. The most widely adopted precision practices have been automated systems related to equipment steering and precise input application, such as autoguidance and section controllers. Once installed, these systems are relatively easy for farmers and their sup... B. Erickson, D.E. Clay, S.A. Clay, S. Fausti |
62. 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 |
63. 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 |
64. 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 |
65. 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 |
66. 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 |
67. 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 |
68. 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 |
69. 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 |
70. 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 |
71. 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 |
72. 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 |
73. 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 |
74. 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 |
75. 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 |
76. 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 |
77. 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 |
78. 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 |
79. 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 |