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1. Determination Of Crop Injury From Aerial Application Of Glyphosate Using Vegetation Indices And GeostatisticsInjury to crops caused by off-target drift of glyphosate can seriously reduce growth and yield, and is of great concern to farmers and aerial applicators. Determining an indirect method for assessing the levels and extent of crop injury could support management decisions. The objectives of this study were to evaluate multiple vegetation indices (VIs) as surrogate variables for glyphosate injury identification and to evaluate the combined use of Geostatistical methods and the VIs to assess... B. Ortiz, S.J. Thomson, Y. Huang, K. Reddy |
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 tracks.... A.F. Colaço, H.J. Rosa, J.P. Molin |
3. 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 |
4. A Precise Fruit Inspection System for Huanglongbing and Other Common Citrus Defects Using GPU and Deep Learning TechnologiesWorld climate change and extreme weather conditions can generate uncertainties in crop production by increasing plant diseases and having significant impacts on crop yield loss. To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this article were 1) to develop a simultaneous image acquisition system using multiple cameras... D. Choi, W. Lee, J.K. Schueller, R. Ehsani, F.M. Roka, M.A. Ritenour |
5. 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 |
6. Assessing Soybean Injury from Dicamba Using RGB and CIR Images Acquired on Small UAVsDicamba 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 applications,... Y. Huang, H. Brand, D. Pennington, K. Reddy, S.J. Thomson |
7. Integrated Approach to Site-specific Soil Fertility ManagementIn precision agriculture the lack of affordable methods for mapping relevant soil attributes is a fundamental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor |
8. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor AlgorithmNitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
9. Toward Geopolitical-Context-Enabled Interoperability in Precision Agriculture: AgGateway's SPADE, PAIL, WAVE, CART and ADAPTAgGateway is a nonprofit consortium of 240+ businesses working to promote, enable and expand eAgriculture. It provides a non-competitive collaborative environment, transparent funding and governance models, and anti-trust and intellectual property policies that guide and protect members’ contributions and implementations. AgGateway primarily focuses on implementing existing standards and collaborating with other organizations to extend them when necessary. In 2010 AgGateway identified... R. Ferreyra, D.B. Applegate, A.W. Berger, D.T. Berne, B.E. Craker, D.G. Daggett, A. Gowler, R.J. Bullock, S.C. Haringx, C. Hillyer, T. Howatt, B.K. Nef, S.T. Rhea, J.M. Russo, S.T. Nieman, P. Sanders, J.A. Wilson, J.W. Wilson, J.W. Tevis, M.W. Stelford, T.W. Shearouse, E.D. Schultz, L. Reddy |
10. 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 comparative... J.L. Casanova, S. Fraile, A. Romo, J. Sanz, C. Moclán |
11. Data Fusion of Imagery from Different Satellites for Global and Daily Crop MonitoringSatellite-based Crop Monitoring is an important tool for decision making of irrigation, fertilization, crop protection, damage assessment and more. To allow crop monitoring worldwide, on a daily basis, data fusion of images taken by different satellites is required. So far, most researches on data fusion focus on retrospective analysis, while advanced crop monitoring capabilities mandate the use of data in real time mode. Therefore, our project goals were: (1) to build a data-fusion online system... O. Beeri, R. Pelta, S. Mey-tal, J. Raz |
12. A Pilot Study on Monitoring Drinking Behavior in Bucket Fed Dairy Calves Using an Ear-Attached Tri-Axial AccelerometerAccelerometers support the farmer with collecting information about animal behavior and thus allow a reduction in visual observation time. The milk intake of calves fed by teat-buckets has not been monitored automatically on commercial farms so far, although it is crucial for the calves’ development. This pilot study was based on bucket-fed dairy calves and intended (1) to evaluate the technical feasibility of using an ear-attached accelerometer (SMARTBOW, Smartbow GmbH, Weibern, Austria)... L. Roland, L. Lidauer, G. Sattlecker, F. Kickinger, W. Auer, V. Sturm, D. Efrosinin, M. Drillich, M. Iwersen, A. Berger |
13. Real-Time Fruit Detection Using Deep Neural NetworksProximal imaging using tractor-mounted cameras is a simple and cost-effective method to acquire large quantities of data in orchards and vineyards. It can be used for the monitoring of vegetation and for the management of field operations such as the guidance of smart spraying systems for instance. One of the most prolific research subjects in arboriculture is fruit detection during the growing season. Estimations of fruit-load can be used for early yield assessments and for the monitoring of... B. Keresztes, J. Da costa, D. Randriamanga, C. Germain, F. Abdelghafour |
14. The Guelph Plot Analyzer: Semi-Automatic Extraction of Small-Plot Research Data from Aerial ImagerySmall-plot trials are the foundation of open-field agricultural research because they strike a balance between the control of an artificial environment and the realism of field-scale production. However, the size and scope of this research field is often limited by the ability to collect data, which is limited by access to labour. Remote sensing has long been investigated to allocate labour more efficiently, therefore enabling the rapid collection of data. Imagery collected by unmanned aerial... J. Nederend, D. Drover, B. Reiche, B. Deen, L. Lee, G.W. Taylor |
15. Field Test of a Satellite-Based Model for Irrigation Scheduling in CottonCotton irrigation in Israel began in the mid-1950s. It is based on an irrigation protocol developed over dozens of years of cotton farming in Israel, and proved to provide among the world's best cotton yield results. In this experiment, we examined the use of an irrigation recommendation system that is based on satellite imagery and hyper-local meteorological data, "Manna treatment", compared to the common irrigation protocols in Israel, which use a crop coefficient (Kc) table and... O. Beeri, S. May-tal, J. Raz, R. Rud |
16. 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 was... 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 |
17. Precision Agriculture: A Paradigm Shift for Espousal of Advanced Farming Practices Among Progressive Farmers in Punjab –PakistanPrecision agriculture provides innovative farm information tools for improved decision making regarding crop growth and yield. Creating awareness for future applications of precision agriculture among progressive farmers in Pakistan was an instrumental force to conduct this study. The purpose was to appraise the awareness level of the respondents for applications of precision agriculture in the field. The objectives such as assessing the awareness level, available information sources, future needs,... E. Ashraf, H.K. Shurjeel, R. Rasheed |
18. 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 crop... R.M. Johnson, B. Ramachandran |
19. A Decision-support Tool to Optimize Mid-season Corn Nitrogen Fertilizer Management from Red, Green, Blue SUAS ImagesCorn receives more nitrogen (N) fertilizer per unit area than any other row crop and optimized soil fertility management is needed to help maximize farm profitability. In Arkansas, N fertilizer for corn is delivered in two- or three-split applications. Three-split applications may provide a better match to crop needs and contribute to minimizing yield loss from N deficiency. However, the total amounts are selected based on soil texture and yield goal without accounting for early-season losses... A. Poncet, T. Bui, W. France, T. Roberts, L. Purcell, J. Kelley |
20. Estimating Spatial and Temporal Variability in Soil Respiration Using UAV-based Multispectral and Thermal Images in an Irrigated Pistachio (Pistachia Vera L.) OrchardSoil respiration (Rs) accounts for the autotrophic and heterotrophic respiration happening in the soil and is a major component of the carbon budget of agricultural ecosystems. Rs is controlled by various interactive factors, including soil moisture, temperature, soil properties, and vegetation productivity. To quantify the carbon budget of climate-smart agriculture systems, it is necessary to understand how irrigation and cover cropping management practices impact... A. Sapkota, M. Roby, C. Chen, I. Kisekka |