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Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
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Authors
Aasen, H
Abdollahi, J.M
Adamchuk, V.I
Adamchuk, V.I
Ahmad, H.N
Alchanatis, V
Alomran, A.M
Alsheri, S.A
Alwabel, M.I
Arzani, H.P
Azimi, M.S
Bajwa, S
Balakrishnan, P
Balakrishnan, P
Balboa, G
Balboa, G
Bareth, G
Bareth, G
Benites, V.D
Bereuter, A
Bernardi, A.C
Blackmer, T.M
Bolten, A
Borhani, M.M
Boyko, Y.I
Brand, H
Bruce, A.E
Brungardt, J.J
Canata, T.F
Cao, Q
Castrignanò, A
Chang, Y.K
Chavan, H
Chen, L
Chen, Y
Chen, Y
Ciampitti, I
Ciampitti, I
Cisneros, M
Colaço, A.F
Connor, J
Corá, J
Cui, B
Cushnahan, T
Daroub, S.H
Denton, A.M
Denton, A.M
Diaz, O.A
Dornbusch, T
EMİNOĞLU, B.M
Esau, T.J
Farahpour, M.D
Farooque, A.A
Farooque, A.A
Farooque, A.A
Ferguson, A
Ferguson, A
Ferguson, R.B
Fiorio, P.R
Francis, D
Franzen, D.W
Franzen, J
Frotscher, K.J
Gan, H
Gerighausen, H
Gianello, E
Gnyp, M.L
Gnyp, M.L
Gonzalez, J
Goorahoo, D
Gowda, H.H
Grafton, M.C
Griffin, T
Griffin, T
Griffin, T
Groulx, D
Guangwei, W
Gérard, B
Han, Y.J
Hendrickson, L
Howatt, K
Huang, S
Huang, W
Huang, Y
Hunt, E
Inamassu, R.Y
Irmak, S
Irwin, M.E
Isakeit, T
Jansen, M
Jasper, J
Jianjun, D
Jin, V
Johnson, R.M
Kaboli, S.D
Kaiser, D
Kanannavar, P
Kanannavar, P
Kereszturi, G
Khalilian, A
Khan, F
Khan, F.S
Khan, F.S
Khosla, R
Khosla, R
Kirkpatrick, T
Kyveryga, P.M
Kyveryga, P.M
L, R.N
Laacouri, A
Lamb, D.W
Le Roux, M
Lee, W
Lenz-Wiedemann, V
Lilienthal, H
Liping, C
Liu, J
Liu, Z
Longchamps, L
Longchamps, L
Madani, A
Madani, A
Maharlooei, M
Maja, J.M
Mangus, D.L
Martello, M
Martinsson, J
McVeagh, P.J
McVeagh, P.J
Miao, Y
Mijatovic, B
Mirdavodi, H.M
Molin, J.P
Monfort, S
Mosmen, E.W
Moulton, H
Mueller, T
Mueller, T
Mulla, D
Nichols, R.L
Nowatzki, J
Nowatzki, J.F
Odvody, G.N
Ortiz-Monasterio, I
Panitzki, M
Pantoja, J.L
Patil, M
Patil, M
Patil, M.B
Patto Pacheco, E
Paulus, S
Payero, J.O
Pearson, R
Pennington, D
Percival, D
Percival, D.C
Piikki, K
Pimstein, A
Prasad, V
Prasad, V
Pritsolas, J
Privette, C.V
Pujari, B
Pujari, B
Pullanagari, R.R
Qiao, X
Rabello, L.M
Reddy, K
Reddy, K.A
Reeg, P.R
Reusch, S
Rienzi, E
Rienzi, E
Rodrigues Jr., F.A
Rodrigues, M
Rodrigues, M
Rondon, S.I
Rudnick, D
Söderström, M
SEYHAN, G.T
Saleem, S.R
Sampson, T
Schacht, R
Scharf, P
Schmer, M
Schneider, D.A
Schnug, E
Schulthess, U
Schumann, A
Schumann, A.W
Schumann, A.W
Seepersad, G
Seepersad, S
Shanwad, U
Sharda, A
Shaver, T
Shirzadi, A
Siegfried, J
Sivarajan, S
Slaeem, S
Smith, L
Song, X
Stadig, H
Stanley, J.N
Stenberg, M
Söderström, M
TALEBPOUR, B
Thomasson, J.A
Thomson, S.J
Tilly, N
Toledo, F.H
Trevisan, R.G
Turner, R.W
TÜRKER, U
Upadhyaya, S
Upadhyaya, S
Van Donk, S
Varela, S
Varela, S
Viator, R.P
Ward, N
Weiqiang, F
White, M
Wienhold, B
Williams, E
Willis, L.A
Xu, J.X
Yang, C
Yang, Q
Yang, Q
Yao, Y
Yegul, U
Yuan, F
Yule, I.J
Yule, I.J
Zaman, Q.U
Zaman, Q.U
Zaman, Q.U
Zamzow, M
Zamzow, M
Zarco-Tejada, P.J
Zhang, Y
Zhao, C
Zhao, T
Zhao, T
Zhijun, M
Zillmann, E
Zur, Y
de Oliveira, R.P
hassanijalilian, O
ÇOLAK, A
Topics
Spatial Variability in Crop, Soil and Natural Resources
Remote Sensing Applications in Precision Agriculture
Type
Poster
Oral
Year
2012
2016
Home » Topics » Results

Topics

Filter results57 paper(s) found.

1. Local And Regional Soil Clay Mapping Using Gamma Ray Spectrometry

... M. Söderström

2. Impact Of Precision Leveling On Spatial Variability Of Moisture Conservation In Arid Zones Of Karnataka

... S. Upadhyaya, P. Balakrishnan, B. Pujari, M. Patil, P. Kanannavar

3. Laser Leveling Holds a Lot Of Promise in Water Conservation and Saving in Dry Zones (Drought Prone Areas) of Karnataka

... S. Upadhyaya, P. Balakrishnan, B. Pujari, M. Patil, P. Kanannavar

4. Long Term Effects of Irrigation with Sewage Effluent on Some Soil Properties

In the arid and semiarid regions, the use of treated sewage water increases as an alternative for non-renewable resources in irrigation. The objective of this research is to identify the effect of irrigation with sewage effluent and well water for lo... M.I. Alwabel, S.A. Alsheri, A.M. Alomran

5. Application of RS, GPS & GIS in a National Monitoring System for Accurate Range Assessment

Sustainable use of rangelands requires information on vegetation cover and its changes through time, condition trend and the effect of climate as well as management practices. The main objective of this research was showing variation of vegetation para... H.P. Arzani, M.S. Azimi, S.D. kaboli, H.M. mirdavodi, M.M. Borhani, J.M. Abdollahi, M.D. farahpour

6. Natural Resources Management through Frontier Technologies - A Case Study from India

The social and economic development of the state is interlaced with our natural resources, and the manner in which they are managed and exploited.  The unplanned development and overexploitation of resources are exerting various... H.H. Gowda, K.A. Reddy, M.B. Patil, R.N. L, U. Shanwad

7. Spatial Variability Index Based On Soil Properties for Notill and Pasture Site-Specific Management in Brazil.

 Quantitative characterization of soil properties spatial variation has first been appl... R.P. De oliveira, A.C. Bernardi, V.D. Benites, L.M. Rabello, R.Y. Inamassu

8. Evaluation of Application Effect of the Laser Land Leveling Technology in Typical Areas of China

The technology of laser land leveling can improve the accuracy of land leveling and it is the important measure of improving irrigation efficiency and facilitating more uniform distribution of irrigation water. The technology is more widely used in China ... W. Guangwei, M. Zhijun, C. Liping, F. Weiqiang, D. Jianjun

9. Winter Wheat Growth Uniformity Monitoring Through Remote Sensed Images

  ... X. Song, C. Zhao, L. Chen, W. Huang, B. Cui

10. Soil Spatial Variability in the Everglades Agricultural Area in South Florida

The Everglades agricultural area is composed by histosols laying on hard limestone bedrock in south Florida. Despite the common assumption of homogeneity of these soils, agricultural practices could result in the increase of soil variability. Therefore, soil spatial variability was studied on three fields (5.5 ha each) at the Everglades Research and Education Center to compare the c... J.L. Pantoja, S.H. Daroub, O.A. Diaz

11. Spatial Econometric Approaches to Develop Site-Specific Nematode Management Strategies in Cotton Production

Root-knot nematode infestations tend to be spatially clustered within agricultural... Z. Liu, T. Griffin, T. Kirkpatrick, S. Monfort

12. Precision Tools to Evaluate Benefits of Tile Drainage in a Corn and Soybean Rotation in Iowa

... P.R. Reeg, T.M. Blackmer, P.M. Kyveryga

13. Analysis of Spatial Variability of Key Soil Attributes In North-Central Ukraine

As Ukrainian agricultural production undergoes major changes, a better understanding of the diversity of land resources is needed to optimize management.  Dealing with large fields (over 100 ha in size) with non-uniform growing conditions presents an opportunity for site-specific management of agricultural inputs. This publication describes our 2010 pilot study on the implementation of integrated mapping of apparent soil electrical conductivity and field topography to guide soil sampling... Y.I. Boyko, V.I. Adamchuk

14. Relationship of Soil Properties to Apparent Ground Conductivity in Wild Blueberry Fields

  One of the fundamental deficiencies in high value crops is the lack of detailed, up-to-date and pertinent geo-referenced soil information for site-specific crop management to improve productivity. This experiment was designed to estimate and map soil properties rapidly and reliably using an electromagnetic induction (EMI) method. Two wild bl... F.S. Khan, Q.U. Zaman, A.W. Schumann, A. Madani, D.C. Percival, A.A. Farooque, S.R. Saleem, F.S. Khan

15. Spatial Variability of Sugarcane Yields in Relation to Soil Salinity in Louisiana

High soil salinity levels have been documented to negatively impact sugarcane yields.  Tests were conducted in commercial sugarcane fields in South Louisiana in 2009-2010 to determine if elevated soil salinity ... R.P. Viator, R.M. Johnson

16. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn

... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk

17. Impact of Variable Rate Fertilization on Nutrients Losses in Surface Runoff for Wild Blueberry Fields

Wild blueberry producers apply agrochemicals uniformly without considering substantial variation in soil properties, topographic features that may affect fruit yield within field. A wild blueberry field was selected to evaluate the impact of variable rate (VR) fertilization on nutrient losses in surface runoff from steep slope to low lying areas to improve cr... S. Slaeem, Q.U. Zaman, A. Madani, A. Schumann, D. Percival, H.N. Ahmad, A.A. Farooque, F. Khan

18. Sensor Fusion on a Wild Blueberry Harvester for Fruit Yield, Plant Height and Topographic Features Mapping to Improve Crop Productivity

  Site-specific crop management can improve profitability and environmental risks of wild blueberry crop having large spatial variation in soil/plant characteristics, topographic features which may affect fruit yield. An integrated automated sensor fusion system including an ultrasonic sensor, a digital color camera, a slope sens... A.A. Farooque, Q.U. Zaman, D. Groulx, A.W. Schumann, T.J. Esau, Y.K. Chang

19. Spatial Apparent Electrical Conductivity (ECa), Soil Moisture and Water Use Efficiency in Vertosol Soils

Producing high resolution maps of water use efficiency (crop yield per unit of water consumption; WUE) for precision crop management is limited by our ability to readily produce maps of soil moi... J.N. Stanley, D.A. Schneider, D.W. Lamb

20. Spatial and Temporal Variability of Corn Grain Yield as a Function of Soil Parameters, and Climate Factors

Effective site-specific management requires an understanding the influence of soil and weather on yield variability. Our objective was to examine the influence of soil, precipitation, and temperature on spatial and temporal corn grain yield variability.  The study site (10 by 250 -m in size) was located in Jaboticabal, São Paulo State, on a Rhodic Hapludox. Corn yield (planted with 0.9-m spacing) was measure... T. Mueller, J. Corá, A. Castrignanò, M. Rodrigues, E. Rienzi

21. On-The-Go pH Sensor: An Evaluation in a Kentucky Field

A commercially available on-the-go soil pH sensor measures and maps subsurface soil pH at high spatial intensities across managed landscapes.  The overall purpose of this project was to evaluate the potential for this sensor to be used in agricultural fields. The specific goals were to determine and evaluate 1) the accuracy with which this instrument can be calibrated, 2) the geospatial structure of soil pH measure... T. Mueller, E. Gianello, B. Mijatovic, E. Rienzi, M. Rodrigues

22. Measurement of Systematic Errors in Crop Prediction

Precision agriculture typically attempts to answer grower questions using an increasingly more fine-grained analysis.  However, some entities, such as cooperatives, can have an interest in answers that are spatially course-grained, such as obtaining an estimate of the overall crop production within a season.  Errors in factors that most influence fine-grained predictions, such as soil quality, may have a smaller impact on overall yield forecasts since their effect is likely to ... A.M. Denton, E.W. Mosmen, J.X. Xu

23. Selection and Utility of Uncooled Thermal Cameras for Spatial Crop Temperature Measurement Within Precision Agriculture

Since previous research used local, single-point measurements to indicate crop water stress, thermography is presented as a technique capable of measuring spatial temperatures supporting its use for monitoring crop water stress. This study investigated measurement accuracy of uncooled thermal cameras under strict environmental conditions, developed hardware and software to implement uncooled thermal cameras and quantified intrinsic properties that impact measurement accuracy and repeatability... D.L. Mangus, A. Sharda

24. Spectral Vegetation Indices to Quantify In-field Soil Moisture Variability

Agriculture is the largest consumer of water globally. As pressure on available water resources increases, the need to exploit technology in order to produce more food with less water becomes crucial. The technological hardware requisite for precise water delivery methods such as variable rate irrigation is commercially available. Despite that, techniques to formulate a timely, accurate prescription for those systems are inadequate. Spectral vegetation indices, especially Normalized Differenc... J. Siegfried, R. Khosla, L. Longchamps

25. High Resolution Hyperspectral Imagery to Assess Wheat Grain Protein in a Farmer's Field

The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexi... F.A. Rodrigues jr., I. Ortiz-monasterio, P.J. Zarco-tejada, F.H. Toledo, U. Schulthess, B. Gérard

26. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)

Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson

27. High Resolution 3D Hyperspectral Digital Surface Models from Lightweight UAV Snapshot Cameras – Potentials for Precision Agriculture Applications

Precision agriculture applications need timely information about the plant status to apply the right management at the right place and the right time. Additionally, high-resolution field phenotyping can support crop breeding by providing reliable information for crop rating. Flexible remote sensing systems like unmanned aerial vehicles (UAVs) can gather high-resolution information when and where needed. When combined with specialized sensors they become powerful sensing systems. Hyp... H. Aasen

28. Detecting Nitrogen Variability at Early Growth Stages of Wheat by Active Fluorescence and NDVI

Low efficiency in the use of nitrogen fertilizer, has been reported around the world which often times result in high production costs and environmental damage. Today, unmanned aerial vehicles (UAV) cameras are being used to obtain conditions of crops, and can cover large areas in a short time. The objectives of this study were (i) to investigate N-variability in wheat at early growth stages using induced fluorescence indices, NDVI measured by active sensor and NDVI obtained by digital i... E. Patto pacheco, J. Liu, L. Longchamps, R. Khosla

29. Comparison Between Tractor-based and UAV-based Spectrometer Measurements in Winter Wheat

In-season variable rate nitrogen fertilizer application needs a fast and efficient determination of nitrogen status in crops. Common sensor-based monitoring of nitrogen status mainly relies on tractor mounted active or passive sensors. Over the last few years, researchers tested different sensors and indicated the potential of in-season monitoring of nitrogen status by unmanned aerial vehicles (UAVs) in various crops. However, the UAV-platforms and the available sensors are not yet accepted t... M. Gnyp, M. Panitzki, S. Reusch, J. Jasper, A. Bolten, G. Bareth

30. Measuring Pasture Mass and Quality Indices Over Time Using Proximal and Remote Sensors

Traditionally pasture has been measured or evaluated in terms of a dry matter yield estimate, which has no reference to other important quality factors. The work in this paper measures pasture growth rates on different slopes and aspects and pasture quality through nitrogen N% and metabolizable energy and ME concentration. It is known that permanent pasture species vary greatly in terms of quality and nutritional value through different stages of maturity. Pasture quality decreases as grass t... I.J. Yule, M.C. Grafton, L.A. Willis, P.J. Mcveagh

31. First Experiences with the European Remote Sensing Satellites Sentinel-1A/ -2A for Agricultural Research

The Copernicus program headed by the European Commission (EC) in partnership with the European Space Agency (ESA) will launch up to twelve satellites, the so called “Sentinels” for earth and environmental observations until 2020. Within this satellite fleet, the Sentinel-1 (microwave) and Sentinal-2 (optical) satellites deliver valuable information on agricultural crops. Due to their high temporal (5 to 6 days repeating time) and spatial (10 to 20 m) resolutions a continuous monit... H. Lilienthal, H. Gerighausen, E. Schnug

32. Planet Labs' Monitoring Solution in Support of Precision Agriculture Practices

Satellite imagery is particularly useful for efficiently monitoring very large areas and providing regular feedback on the status and productivity of agricultural fields. These data are now widely used in precision farming; however, many challenges to making optimal use of this technology remain, such as easy access to data, management and exploitation of large datasets with deep time series, and sharing of the data and derived analytics with users. Providing satellite imagery through a cloud... K.J. Frotscher, R. Schacht, L. Smith, E. Zillmann

33. Comparison Between High Resolution Spectral Indices and SPAD Meter Estimates of Nitrogen Deficiency in Corn

Low altitude remote sensing provides an ideal platform for monitoring time sensitive nitrogen status in crops. Research is needed however to understand the interaction between crop growth stage, spatial resolution and spectral indices derived from low altitude remote sensing. A TetraCam camera equipped with six bands including the red edge and near infrared (NIR) was used to investigate corn nitrogen dynamics. Remote sensing data were collected during the 2013 and 2014 growing seasons at four... D. Mulla, A. Laacouri, D. Kaiser

34. A Photogrammetry-based Image Registration Method for Multi-camera Systems

In precision agriculture, yield maps are important for farmers to make plans. Farmers will have a better management of the farm if early yield map can be created. In Florida, citrus is a very important agricultural product. To predict citrus production, fruit detection method has to be developed. Ideally, the earlier the prediction can be done the better management plan can be made. Thus, fruit detection before their mature stage is expected. This study aims to develop a thermal-visible camer... H. Gan, W. Lee, V. Alchanatis

35. Potential Improvement in Rice Nitrogen Status Monitoring Using Rapideye and Worldview-2 Satellite Remote Sensing

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large area in a timely fashion. Satellite remote sensing provides a promising technology for crop growth monitoring and precision management over large areas. The FORMOSAT-2 satellite remote sensing imageries with 4 wavebands have been used to estimate rice N status. The objective of this study was to evaluate the potential of using high spatial ... S. Huang, Y. Miao, F. Yuan, M.L. Gnyp, Y. Yao, Q. Cao, V. Lenz-wiedemann, G. Bareth

36. CropSAT - a Public Satellite-based Decision Support System for Variable-rate Nitrogen Fertilization in Scandinavia

CropSAT is a free-to-use web application for satellite-based production of variable-rate application (VRA) files of e.g. nitrogen (N) and fungicides currently available in Sweden and Denmark. Even in areas frequently covered by clouds, vegetation index maps from data derived from low-cost or freely available optical satellites can be used in practice as a cost-efficient tool in time-critical applications such as optimized nitrogen use. During the very cloudy year 2015, or more useable ima... M. Söderström, H. Stadig, J. Martinsson, M. Stenberg, K. Piikki

37. Measuring Height of Sugarcane Plants Through LiDAR Technology

Sugarcane (Saccharum spp.) has an important economic role in Brazilian agriculture, especially in São Paulo State. Variation in the volume of plants can be an indicative of biomass which, for sugarcane, strongly relates to the yield. Laser sensors, like LiDAR (Light Detection and Ranging), has been employed to estimate yield for corn, wheat and monitoring forests. The main advantage of using this type of sensor is the capability of real-time data acquisition in a non-destructive way, p... T.F. Canata, J.P. Molin, A.F. Colaço, R.G. Trevisan, P.R. Fiorio, M. Martello

38. Window-based Regression Analysis of Field Data

High-resolution satellite and areal imagery enables multi-scale analysis that has previously been impossible.  We consider the task of localized linear regression and show that window-based techniques can return results at different length scales with very high efficiency.  The ability of inspecting multiple length scales is important for distinguishing factors that vary over different length scales.  For example, variations in fertilization are expected to occur on shorter len... A.M. Denton, H. Chavan, D.W. Franzen, J.F. Nowatzki

39. Hyperspectral Imaging to Measure Pasture Nutrient Concentration and Other Quality Parameters

Managing pasture nutrient requirements on large hill country sheep and beef properties based on information from soil sampling is expensive because of the time and labor involved. High levels of error are also expected as these properties are often greatly variable and it is therefore extremely difficult to sample intensively enough to capture this variation. Extensive sampling was also not considered viable as there was no effective means of spreading fertilizer with a variable rate capabili... I.J. Yule, R.R. Pullanagari, G. Kereszturi, M.E. Irwin, P.J. Mcveagh, T. Cushnahan, M. White

40. Creating Prescription Maps from Historical Imagery for Site-specific Management of Cotton Root Rot

Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is a severe plant disease that has affected cotton production for over a century. Recent research found that a commercial fungicide, Topguard (flutriafol), was able to control this disease. As a result, Topguard Terra Fungicide, a new and more concentrated formulation developed specifically for this market was registered in 2015, so cotton producers can use this product to control the disease. Cotton root rot only inf... C. Yang, G.N. Odvody, J.A. Thomasson, T. Isakeit, R.L. Nichols

41. Retrieving Crops' Quantitative Biophysical Parameters Through a Newly Developed Multispectral Sensor for UAV Platforms

Today’s intensive agricultural production needs to increase its efficiency in order to keep its profitability in the current market of decreasing prices on one hand, and to reduce the environmental impact on the other. Crop growers are starting to adopt side dressing nitrogen fertilization as part of their fertilization programs, for which they need accurate information about biomass development and nitrogen condition in the crop. This information is usually acquired through ground samp... A. Pimstein, Y. Zur, M. Le roux

42. Development of Sensor Reflection Indices To Predict Yield And Protein Content Based On In-Season N Status

Environmental and economic demands make it necessary for farmers to adopt   management systems that improve Nitrogen Use Efficiency. The premium paid to producers has made farmers striving for maximum grain protein levels because protein is a very important quality component of grains and an important attribute in the market place. The protein content of wheat grains approximately ranges from 8 to 20%. The optimization of nitrogen (N) fertilization is the object of intense research ... U. Yegul, B. Talebpour, U. TÜrker, B.M. EmİnoĞlu, G.T. Seyhan, A. Çolak

43. Intuitive Image Analysing on Plant Data - High Throughput Plant Analysis with Lemnatec Image Processing

For digital plant phenotyping huge amounts of 2D images are acquired. This is known as one part of the phenotyping bottleneck. This bottleneck can be addressed by well-educated plant analysts, huge experience and an adapted analysis software. Automated tools that only cover specific parts of this analysis pipeline are provided. During the last years this could be changed by the image processing toolbox of LemnaTec GmbH. An automated and intuitive tool for the automated analysis of huge amount... S. Paulus, T. Dornbusch, M. Jansen

44. In Season Estimation of Barley Biomass with Plant Height Derived by Terrestrial Laser Scanning

The monitoring of plant development during the growing season is a fundamental base for site-specific crop management. In this regard, the amount of plant biomass at a specific phenological stage is an important parameter to evaluate the actual crop status. Since biomass is directly only determinable with destructive sampling, methods of recording other plant parameters, such as crop height or density, which are suitable for reliable estimations are increasingly researched. Over the past two ... N. Tilly

45. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)

Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson

46. Assessing Soybean Injury from Dicamba Using RGB and CIR Images Acquired on Small UAVs

Dicamba is an herbicide used for postemegence control of several broadleaf weeds in corn, grain sorghum, small grains, and non-cropland. Currently, dicamba-tolerant (DT) soybean and cotton are under development, which provide new options to combat weeds resistant to glyphosate, the most widely used herbicide.  With the use of DT-trait cotton and soybean, off-target dicamba drift onto susceptible crops will become a concern. To relate soybean injury to different rates of dicamba applicati... Y. Huang, H. Brand, D. Pennington, K. Reddy, S.J. Thomson

47. Utilizing Space-based Technology for Cotton Irrigation Scheduling

Accurate soil moisture content measurements are vital to precision irrigation management. Electromagnetic sensors such as capacitance and time domain reflectometry have been widely used for measuring soil moisture content for decades. However, to estimate average soil moisture content over a large area, a number of ground-based in-situ sensors would need to be installed, which would be expensive and labor intensive. Remote sensing using the microwave spectrum (such as GPS signals) has been us... A. Khalilian, X. Qiao, J.O. Payero, J.M. Maja, C.V. Privette, Y.J. Han

48. Greenhouse Study to Identify Glyphosate-resistant Weeds Based on Canopy Temperature

Development of herbicide-resistant crops has resulted in significant positive changes to agronomic practices, while repeated and intensive use of herbicides with the same mechanisms of action has caused the development of herbicide-resistant weeds. As of 2015, 35 weed species are reported to be resistant to glyphosate worldwide. A greenhouse study was conducted to identify characteristics which can be helpful in field mapping of glyphosate resistant weeds by using UAV imagery. The experiment ... A. Shirzadi, M. Maharlooei, O. Hassanijalilian, S. Bajwa, K. Howatt, S. Sivarajan, J. Nowatzki

49. Challenges and Successes when Generating In-season Multi-temporal Calibrated Aerial Imagery

Digital aerial imagery (DAI) of the crop canopy collected by aircraft and unmanned aerial vehicles is the yardstick of precision agriculture.  However, the quantitative use of this imagery is often limited by its variable characteristics, low quality, and lack of radiometric calibration.  To increase the quality and utility of using DAI in crop management, it is important to evaluate and address these limitations of DAI.  Even though there have been improvements in spatial reso... P.M. Kyveryga, J. Pritsolas, J. Connor, R. Pearson

50. Detection of Potato Beetle Damage Using Remote Sensing from Small Unmanned Aircraft Systems

Remote sensing with small unmanned aircraft systems (sUAS) has potential applications in agriculture because low flight altitudes allow image acquisition at very high spatial resolution.  We set up experiments at the Oregon State University Hermiston Agricultural Research and Extension Center (HAREC) to assess advantages and disadvantages of sUAS for precision farming. In 2014, we conducted an experiment in irrigated potatoes with 4 levels of artificial infestation by Colorado Potato Bee... E. Hunt, S.I. Rondon, A.E. Bruce, R.W. Turner, J.J. Brungardt

51. Time Series Analysis of Vegetation Dynamics and Burn Scar Mapping at Smoky Hill Air National Guard Range, Kansas Using Moderate Resolution Satellite Imagery

Military installments are import assets for the proper training of armed forces. To ensure the continued viability of the training grounds, management practices need to be implemented to sustain the necessary environmental conditions for safe and effective training. This analysis uses satellite imagery over time to gain insight into vegetation conditions over a large military installment. MODIS imagery was collected multiple times a year for 11 years at Smoky Hill Air National Guard Range (Sm... E. Williams

52. Melon Classification and Segementation Using Low Cost Remote Sensing Data Drones

Object recognition represents currently one of the most developing and challenging areas of the Computer Vision. This work presents a systematic study of various relevant parameters and approaches allowing semi-automatic or automatic object detection, applied onto a study case of melons on the field to be counted. In addition it is of a cardinal interest to obtain the quantitative information about performance of the algorithm in terms of metrics the suitability whereof is determined by the f... T. Zhao, Y. Chen, J. Franzen, J. Gonzalez, Q. Yang

53. Aerial Photographs to Predict Yield Loss Due to N Deficiency in Corn

Nitrogen fertilizer is a crucial input for corn production, and in the U.S. more nitrogen is applied to corn than to all other crops combined.  In wet weather, nitrogen can be lost from soil by leaching and by denitrification.  Which process predominates depends largely on soil drainage.  Nitrogen deficiency in nearly any plant is expressed by a lighter green color of leaves than in nitrogen-sufficient plants.  Nitrogen deficiency in corn can be easily seen from the air.&n... P. Scharf

54. Almond Canopy Detection and Segmentation Using Remote Sensing Data Drones

The development of Unmanned Aerial System (UAV) makes it possible to take high resolution images of trees easily. These images could help better manage the orchard. However, more research is necessary to extract useful information from these images. For example, irrigation schedule and yield prediction both rely on accurate measurement of canopy size. In this paper, a workflow is proposed to count trees and measure the canopy size of each individual tree. The performances of three different m... T. Zhao, M. Cisneros, Y. Chen, Q. Yang, Y. Zhang

55. AGOC: Agriculture Operations Center

After another long day, the farmer sits down in front of a computer (wishing this time was instead spent on the front porch catching a last glimpse of the sunset), and reflects once again ...     What if   ...  I actually knew the health of 100% of my crops rather than what I know today. a mere 20%. What if   ...  there was an effective, simple way to synchronize crop scouting and crop imagery efforts. ... M. Zamzow, H. Moulton

56. The Agriculture Operations Center: the Answer to “What If...”

Can’t farming be simpler?  Yes…with an Agriculture Operations Center -- we call it the AGOC, and it’s the next big step for precision agriculture.  Leveraging decades of lessons from the US Air Force, the AGOC provides the ability to schedule, execute, collect, consolidate, and distribute all the support a farmer needs from satellites, piloted aircraft, unmanned aircraft, sensing, modeling, and analysis…all packaged into “one stop shopping.”&nbs... M. Zamzow

57. Precision Agriculture Techniques for Crop Management in Trinidad and Tobago: Methodology & Field Layout

Agriculture in Trinidad and Tobago has not advanced at the same rate at which new agricultural technology has been released. This has led to large-scale abandonment of crop lands as challenges posed by labor availability and their agronomic capability could not meet the technological demands for agricultural production, competitiveness and sustainability. There is an urgent need to develop technology-based agriculture models to meet the demands of a modern agricultural sector and to maintain ... G. Seepersad, T. Sampson, S. Seepersad, D. Goorahoo