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1. Low Cost High-resolution Aerial Photogrammetric Techniques For Precision Agriculture In Latin American CountriesOne of the first steps in precision agriculture is to obtain aerial images of an area of interest to determine soil units and management zones. Aerial and remote sensing information, digital elevation models and other spatial data are often inexistent in planning offices in Latin American countries and, up to now, enhancement and modifications have not been integrated into smaller scaled planning operation such as farming. High resolution remote sensing images from scanning satellites like Quickbird,... J.S. Perret, O.E. arriaza, M.E. D, J. Aguilar |
2. Early Identification Of Leaf Rust On Wheat Leaves With Robust Fitting Of Hyperspectral SignaturesEarly recognition of pathogen infection is of great relevance in precision plant protection. Disease detection before the occurrence of visual symptoms is of particular interest. By use of a laserfluoroscope, UV-light induced fluorescence data were collected from healthy and with leaf rust infected wheat leaves of the susceptible cv. Ritmo 2-4 days after inoculation under controlled conditions. In order to evaluate disease impact on spectral characteristics 215 wavelengths in the range of 370-800... C. R, T. Rumpf, K. B, M. Hunsche, L. Pl, G. Noga |
3. Changes Of Data Sampling Procedure To Avoid Energy And Data Losses During Microclimates Monitoring With Wireless Sensor Networks... J.C. Benavente, C.E. Cugnasca, M.F. Barros, H.P. Santos, G. Http://icons.paqinteractive.com/16x16/ac |
4. An Inter-connection Model Between Standard Zigbee And Isobus Network (ISO11783)The typical five-step cyclical process of precision agriculture includes soil and environment data collection, diagnosis, data analysis, precision field correction operation and evaluations. Usually, some steps are executed in field, others in the farm office and others in both. This can result in a complex system and consequently in waste of time and high cost in equipment, tools and workmanship. To simplify this process, the challenge is running... M.F. Barros, C.E. Cugnasca, J. Congona benavente |
5. Soil Organic Carbon Maintenance Requiremnets And Mineralizatyion Rate Constants: Site Specific CalcuationsOver the past 100 years numerous studies have been conducted with the goal of quantifying the impact of management on carbon turnover. It is difficult to conduct a mechanistic evaluation of these studies because each study was conducted under unique soil, climatic, and management conditions. Techniques for directly comparing data from unique studies are needed. This study discusses techniques for comparing data collected... D.E. Clay, G. Carlson, J. Tatge |
6. A Computer Decision Aid For The Cotton Precision Agriculture Investment DecisionThis article introduces the Cotton Precision Agriculture Investment Decision Aid (CPAIDA), a software decision tool for analyzing the precision agriculture investment decision. CPAIDA was developed to provide improved educational information about precision farming equipment ownership costs, and the required returns to pay for their investment. The partial budgeting and breakeven analysis framework is documented along with use of the decision aid. With care in specifying values, program users... J.A. Larson, D.F. Mooney, R.K. Roberts, B.C. English |
7. Cotton Precision Farming Adoption In The Southern United States: Findings From A 2009 SurveyThe objectives of this study were 1) to determine the status of precision farming technology adoption by cotton producers in 12 states and 2) to evaluate changes in cotton precision farming technology adoption between 2000 and 2008. A mail survey of cotton producers located in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, Tennessee, Texas and Virginia was conducted in February and March of 2009 to establish the use of precision farming technologies... M. Velandia, D.F. Mooney, R.K. Roberts, B.C. English, J.A. Larson, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, C. Wang, E. Segarra, J.M. Reeves |
8. Adoption And Perceived Usefulness Of Precision Soil Sampling Information In Cotton ProductionSoil testing assists farmers in identifying nutrient variability to optimize input placement and timing. Anecdotal evidence suggests that soil test information has a useful life of 3–4 years. However, perceived usefulness may depend on a variety of factors, including field variability, farmer experience and education, farm size, Extension, and factors indirectly related to farming. In 2009, a survey of cotton farmers in 12 Southeastern states collected information... D.C. Harper, D.M. Lambert, B.C. English, J.A. Larson, R.K. Roberts, M. Velandia, D.F. Mooney, S.L. Larkin |
9. Effect Of Nitrogen Application Rate On Soil Residual N And Cotton YieldA long-term study was conducted on nitrogen application rate and its impact on soil residual nitrogen and cotton (FM960B2RF) lint yield under a drip irrigation production system near Plainview, Texas. The experiment was a randomized complete block design with five nitrogen application rates (0, 56, 112, 168 and 224 kg per ha) and five replications. The soil nitrogen treatment was applied as side dressing. Cotton yield, leaf N, seed N, soil residual nitrate, amount of irrigation, and rainfall data... M. Parajulee, D. Neupane, C. Wang, S. Carroll, R. Shrestha |
10. Use of Corn Height to Improve the Relationship Between Active Optical Sensor Readings and Yield EstimatesPre-season and early in-season loss of N continues to be a problem in corn. One method to improve nitrogen use efficiency is to fertilize based on in-season crop foliage sensors. The objective of this study was to evaluate two different ground-based, active-optical sensors and explore the use of corn height with sensor readings for improved relationship with corn yield. Two different ground-based active-optical sensors (GreenseekerTM and... L. Sharma, D.W. Franzen |
11. Spectral Models for Estimation of Chlorophyll Content, Nitrogen, Moisture Stress and Growth of Wheat CropField experiments were conducted during 2009-10 and 2010-11 at research farm of the department of Farm Machinery and Power Engineering, Punjab Agricultural university, Ludhiana. Three wheat ... B.S. Sekhon, J. Mukherjee, A. Sharma, S.K. Thind, R. Kaur, M.S. Makkar |
12. Pesticide Drift Control with Wireless Sensor NetworksPrecision Agriculture is an agricultural practice that uses technology based on the principle of variability. The geographically referenced data implement the process of agricultural automation so as to dose fertilizers and pesticides. The efficient application of low cost pesticides without contamination the environment is an agricultural production challenge. The main effect to be avoided during application is pesticide drift. To minimize it is important to know the environmental conditions,... C.E. Cugnasca, I.M. Santos |
13. Computer Aided Engineering Analysis and Design Optimization for Precision Manufacturing of Tillage Tool: Sweep CultivatorThe process optimization in advance tillage tool system conceptually designed and fabricated by computer aided engineering analysis techniques. The Software testing a field performance is taken in the soil bed preparation as well as in the various crop patterns. It was found most use full in obtaining high weed removal efficiency. The precision geometry, optimum energy utilization, multi-operational design, easy transport and flexible attachments are some of the features which results in achieving... G.U. Shinde, D.M. Salokhe, P.D. Badgujar, D.B. Sharma |
14. The Adoption of Information Technologies and Subsequent Changes in Input Use in Cotton ProductionThe use of precision farming has become increasingly important in cotton production. It allows farmers to take advantage of knowledge about infield variability by applying expensive inputs at levels appropriate to crop needs. Essential to the success of the precision... N.M. Thompson, J.A. Larson, B.C. English, D.M. Lambert, R.K. Roberts, M. Velandia, C. Wang |
15. Adoption and Non-Adoption of Precision Farming Technologies by Cotton FarmersWe used the 2009 Southern Cotton Precision Farming Survey data collected from farmers in twelve U.S. states (Alabama, Arkansas, Florida, Georgia, Louisiana, Missouri, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia) to identify reasons on why some adopt and others do not adopt precision farming techniques. Those farmers who provided the cost as the reason for non-adoption are farmers characterized by lower education... A.K. Mishra, M. Pandit, K.P. Paudel, E. Segarra |
16. Climatological Diagnostic Analysis: A Case Study for Parbhani District in Marathwada Region of India... S.N. Pawar, A.K. Gore, G.U. Shinde, M.S. Pendke |
17. John Deere FarmSight™Agriculture has had several revolutions in the past century, and it currently faces what may be its greatest challenge to date – population growth and the increased need for food, fiber, and fuel in the future. To meet this challenge the agricultural industry will have to drive efficiencies to a level never seen before, within a context of several macro trends (e.g., farm sizes increasing, environmental sustainability requirements evolving). John Deere FarmSightTM... M. Stelford |
18. Radio Frequency Identification For Implementing Traceability In The Cotton Production In The Brazilian MidwestAccording to the International Cotton Advisory Committee - ICAC projection for the fiber in cotton production for the crop year 2012/2013 is expected to reach an amount of 15.19 million tons , according to a forecast released in August 2012 . In the Brazilian context , according to the Ministry of Agriculture, Livestock and Supply of Brazil cotton cultivation in Brazil has grown especially in the Midwest . In particular , exports of cotton fiber increased twice in one season in 2003/2004... C. Santos, E.O. Weschter, M.A. Dota, C.E. Cugnasca |
19. Active Optical Sensor Algorithms For Corn Yield Prediction And In-Season N Application In North DakotaA recent series of seventy seven field N rate experiments with corn (Zea mays, L.) in North Dakota was conducted. Multiple regression analysis of the characteristics of the data set indicated that segregating the data into those with high clay soils and those with medium textures increased the relationship between N rate and corn yield. However, the nearly linear positive slope relationship in high clay soils and coarser texture soils with lower yield productivity indicated... L. Sharma, H. Bu, R. Ashley, G. Endres, J. Teboh, D.W. Franzen |
20. Application Of Infrared Thermography For Assessing Beef Cattle Comfort Using A Fuzzy Logic Classifier... L.S. Martello, T.F. Canata, R.V. Sousa |
21. Use of Unmanned Aerial Vehicles to Inform Herbicide Drift AnalysisA primary advantage of unmanned aerial vehicle-based imaging systems is responsiveness. Herbicide drift events require prompt attention from a flexible collection system, making unmanned aerial vehicles a good option for drift analysis. In April 2015, a drift event was documented on a Mississippi farm. A combination of corn and rice fields exhibited symptomology consist with non-target injury from a tank mix of glyphosate and clethodim. An interesting observation was the... J.M. Prince czarnecki, D.B. Reynolds, R.J. Moorhead |
22. 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 were... L. Maldaner, J.P. Molin, T.F. Canata |
23. Measuring Height of Sugarcane Plants Through LiDAR TechnologySugarcane (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, previously... T.F. Canata, J.P. Molin, A.F. Colaço, R.G. Trevisan, P.R. Fiorio, M. Martello |
24. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton ProductionThis research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler |
25. Soil Spatial Variability Assessment and Precision Nutrient Management in Maize (Zea Mays L.)Investigations on soil spatial variability and precision nutrient management based targeted yield approach in maize was carried out at Agricultural research station (ARS), Mudhol (Karnataka), India under irrigated condition during 2013-14, 2014-15 and 2015-16. ARS, Mudhol is located in northern dry zone of Karnataka at 160 20! N latitude, 750 15! E longitude and at an altitude of 577.6 meter above mean sea level. To assess the spatial variability, the study area was divided into 20 x20 m size... M.P. Potdar, G.B. Balol, S.A. Satyareddi, B.T. Nadagouda , C.P. Chandrashekar |
26. Delineation of Site-Specific Nutrient Management Zones to Optimize Rice Production Using Proximal Soil Sensing and Multispectral ImagingEvaluating nutrient uptake and site-specific nutrient management zones in rice in Costa Rica from plant tissue and soil sampling is expensive because of the time and labor involved. In this project, a range of measurement techniques were implemented at different vintage points (soil, plant and UAVs) in order to generate and compare nutrient management information. More precisely, delineation of site-specific nutrient management zones were determined using 1) georeferenced soil/tissue... J.E. Villalobos, J.S. Perret, K. Abdalla, C.L. Fuentes, J.C. Rodriguez, W. Novais |
27. An On-farm Experimental Philosophy for Farmer-centric Digital InnovationIn this paper, we review learnings gained from early On-Farm Experiments (OFE) conducted in the broadacre Australian grain industry from the 1990s to the present day. Although the initiative was originally centered around the possibilities of new data and analytics in precision agriculture, we discovered that OFEs could represent a platform for engaging farmers around digital technologies and innovation. Insight from interacting closely with farmers and advisors leads us to argue for a change... S. Cook, M. Lacoste, F. Evans, M. Ridout, M. Gibberd, T. Oberthur |
28. Machine Monitoring As a Smartfarming Concept ToolCurrent development trends are associated with the digitization of production processes and the interconnection of individual information layers from multiple sources into common databases, contexts and functionalities. In order to automatic data collection of machine operating data, the farm tractors were equipped with monitoring units ITineris for continuous collection and transmission of information from tractors CAN Bus. All data sets are completed with GPS location data. Acreage... M. Kroulik, V. Brant, P. Zabransky, J. Chyba, V. Krcek, M. Skerikova |
29. AgronomoBot: A Smart Answering Chatbot Applied to Agricultural Sensor NetworksMobile devices advanced adoption has fostered the creation of various messaging applications providing convenience and practicality in general communication. In this sense, new technologies arise bringing automatic, continuous and intelligent features for communication through messaging applications by using web robots, also called Chatbots. Those are computer programs that simulate a real conversation between humans to answer questions or do tasks, giving the impression that the person is talking... G.M. Mostaço, L.B. Campos, C.E. Cugnasca, I.R. Souza |
30. Application of Routines for Automation of Geostatistical Analysis Procedures and Interpolation of Data by Ordinary KrigingOrdinary kriging (OK) is one of the most suitable interpolation methods for the construction of thematic maps used in precision agriculture. However, the use of OK is complex. Farmers/agronomists are generally not highly trained to use geostatistical methods to produce soil and plant attribute maps for precision agriculture and thus ensure that best management approaches are used. Therefore, the objective of this work was to develop and apply computational routines using procedures and geostatistical... N.M. Betzek, E.G. Souza, C.L. Bazzi, P.G. Magalhães, A. Gavioli, K. Schenatto, R.W. Dall'agnol |
31. Use of Precision Technologies to Conduct Successful Within-field, On-farm TrialsPerforming randomized replicated trials in row crop field environments has the potential to increase crop production in environmentally sustainable ways. Successful implementation requires an understanding of implement capabilities and sources of potential systematic error, including operator error. Equipment capabilities can be thought of as a series of several critical “links in a chain,” each with implications that propagate downstream. We will... M. Stelford, A. Krmenec |
32. 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 University... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness |
33. How Digital is Agriculture in South America? Adoption and LimitationsA rapidly growing population in a context of land and water scarcity, and climate change has driven an increase in healthy, nutritious, and affordable food demand while maintaining the current cropping area. Digital agriculture (DA) can contribute solutions to meet the demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. This article presents the results from a systematic review... G. Balboa, L. Puntel, R. Melchiori, R. Ortega, G. Tiscornia, E. Bolfe, A. Roel, F. Scaramuzza, S. Best, A. Berger, D. Hansel, D. Palacios |
34. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US MidwestEffective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly across... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan |
35. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and IndianaPrecision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota.... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor |
36. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in ArgentinaThe lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomist... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito |
37. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-upThe concentration of heavy metals that needs to be maintained in aquaponic environments for habitable growth of plants has been a cause of concern for many decades now as it is not possible to eliminate them completely in a commercial set-up. Our goal is to design a cost-effective real-time smart sensing and actuation system in order to control the concentration of heavy metals in aquaponic solutions. Our solution consists of sensing the nutrient concentrations in the aquaponic solution, namely... S. Dhal, J. Louis, N. O'sullivan, J. Gumero, M. Soetan, S. Kalafatis, J. Lusher, S. Mahanta |