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| Filter results17 paper(s) found. |
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1. Categorization of Districts Based on Nonexchangeable Potassium: Generation GIS Maps and Implications in Efficient K Fertility Management in Indian AgricultureRecommendations of K fertilizer are made based on available (exchangeable + water soluble) K status only in India and other despite of substantial contribution of nonexchangeable fraction of soil K to crop K uptake. Present paper examines the information generated in the last 30 years on the status of nonexchangeable K in Indian soils, categorization of Indian soils based on exchangeable and nonexchangeable K fractions and making K recommendations. Data for both K fractions of different... C. Srinivasa rao, K. Rao, H. Magen, B. Venkateswarlu, A. Subba rao |
2. In-Field Corn Stalk Location Using Rapid Line-Scan Technique... Y. Shi, N. Wang |
3. Sensing The Inter-row For Real-time Weed Spot Spraying In Conventionally Tilled Corn FieldsThe spatial distribution of weeds is aggregated most of the time in crop fields. Site-specific management of weeds could result in economical and environmental benefits due to herbicide... L. Longchamps, B. Panneton, M. Simard, R. Theriault, T. Roger |
4. Partial Weed Scouting For Exhaustive Real-time Spot Spraying Of Herbicides In CornReal-time spot spraying of weeds implies the use of plant detectors ahead of a sprayer. The range of weed spatial autocorrelation perpendicularly to crop rows is often greater than the space between the corn rows. To assess the possibility of using less than one plant detector scouting each inter-row, a one hectare field was entirely sampled with ground pictures at the appropriate timing for weed spraying. Different ways of disposing the detectors ahead of the sprayer were virtually tested. Scouting... L. Longchamps, B. Panneton, G.D. Leroux, M. Simard, R. Theriault |
5. Performance Evaluation Of Off-shelf Range Sensors For In-field Crop Height MeasurementAbstract: In-season plant height is a good predictor of yield potential, which needs to be measured with techniques of high spatial resolution and accuracy. In this study, systematic performance evaluations were conducted on three types of commercial range sensors, an ultrasonic sensor, a laser range finder and a range camera on plant height measurement, under laboratory and field conditions. Results showed that the average errors between the measured heights... N. Wang, Y. Shi, R.K. Taylor |
6. Experiencs Of Extension Education Via Online Delivery Of Programming Related To Precision Agriculture TechnologiesThis paper will describe the content and experiences teaching an extension education course on precision agriculture technologies via online delivery. The course was developed to be delivered in 16 weeks meeting one time a week online. There was also a one-day face-to-face hands-on session focused around 4 lab type activities related to GPS guidance, diagnosis, and setup and maximizing the usefulness of precision agriculture technologies. This course focuses on agricultural... D.K. Shannon |
7. Development Of An Index-Based Insurance Product: Validation Of A Forage Production Index Derived From Medium Spatial Resolution fCover Time SeriesAn index-based insurance solution is developed by Pacifica Crédit Agricole Assurances and Astrium GEO-Information to estimate and monitor the near real-time forage production in France. In this system, payouts are indexed on an indicator, called Forage Production Index (FPI), calculated using a biophysical characterization of the grassland from medium spatial resolution remote sensing time series. We used the Fraction of green Vegetation Cover (fCover) integral as... A. Jacquin, G. Sigel, O. Hagolle, B. Lepoivre, A. Roumiguié, H. Poilvé |
8. Precision Nutrient Management For Enhancing The Yield Of Groundnut In Peninsular IndiaGroundnut is an important oil seed crop grown in an area of around 8 lakh hectares in Karnataka state of India under rainfed conditions. In these situations farmers applied inadequate fertilizer without knowing the initial nutrient status of the soil which resulted in low nutrient use efficiency that intern lead to low productivity of groundnut in these areas. Soil fertility deterioration due to... M. Giriyappa, T. Sheshadri, D. Hanumanthappa, M. Shankar, S.B. Salimath, T. Rudramuni, N. Raju, N. Devakumar, G. Mallikaarjuna, M.T. Malagi, S. Jangandi |
9. 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 viable... N.M. Betzek, E.G. Souza, C.L. Bazzi, K. Schenatto, A. Gavioli, M.F. Maggi |
10. Delineation of Site-specific Management Zones Using Spatial Principal Components and Cluster AnalysisThe delineation of site-specific management zones (MZs) can enable economic use of precision agriculture for more producers. In this process, many variables, including chemical and physical (besides yield data) variables, can be used. After selecting variables, a cluster algorithm like fuzzy c-means is usually applied to define the classes. Selection of variables comprise a difficult issue in cluster analysis because these will often influence cluster determination. The goal of this study was... A. Gavioli, E.G. Souza, C.L. Bazzi, N.M. Betzek, K. Schenatto, H. Beneduzzi |
11. EZZone - An Online Tool for Delineating Management ZonesManagement zones are a pillar of Precision Agriculture research. Spatial variability is apparent in all fields, and assessing this variability through measurement devices can lead to better management decisions. The use of Geographic Information Systems for agricultural management is common, especially with management zones. Although many algorithms have been produced in research settings, no online software for management zone delineation exists. This research used a common... G. Vellidis, C. Lowrance, S. Fountas, V. Liakos |
12. Variable Selection and Data Clustering Methods for Agricultural Management Zones DelineationDelineation of agricultural management zones (MZs) is the delimitation, within a field, of a number of sub-areas with high internal similarity in the topographic, soil and/or crop characteristics. This approach can contribute significantly to enable precision agriculture (PA) benefits for a larger number of producers, mainly due to the possibility of reducing costs related to the field management. Two fundamental tasks for the delineation of MZs are the variable selection and the cluster analysis.... A. Gavioli, E.G. Souza, C.L. Bazzi, N.M. Betzek, K. Schenatto |
13. 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 |
14. Partial Fruitlet Cutting Approach for Robotic Apple ThinningEarly season thinning of apple fruitlets is a crucial task in commercial apple farming, traditionally accomplished through chemical sprays or labor-intensive manual operations. These methods, however, are faced with the challenges of diminishing labor availability as well as environmental and/or economic sustainability. This research examines 'partial fruitlet cutting,' a novel nature-assisted strategy, as an alternative method for automated apple thinning in orchards. The study hypothesized... R. Sapkota, M. Karkee |
15. Onboard Weed Identification and Application Test with Spraying Drone SystemsCommercial spraying drone systems nowadays have the ability to implement variable rate applications according to pre-loaded prescription maps. Efforts are needed to integrate sensing and computing technologies to realize on-the-go decision making such as those on the ground based spraying systems. Besides the understudied subject of drone spraying pattern and efficacy, challenges also exist in the decision making, control, and system integration with the limits on payload and flight endurance... Y. Shi, M. Islam, K. Steele, J.D. Luck, S. Pitla, Y. Ge, A. Jhala, S. Knezevic |
16. Remote and Proximal Sensing for Sustainable Water Use in Almond Orchards in Southeast Spain in a Digital Farming ContextThe increasing expansion of irrigated almond orchards in regions of southeast Spain, facing water scarcity, underscores the need for a more effective and precise monitoring of the crop water status to optimize irrigation scheduling and improve crop water use efficiency. Remote and proximal sensing, combining visible, multispectral and thermal capabilities at different scales allows to estimate water needs, detect and quantify crop water stress, or identify different productivity zones within an... |
17. Driving Growth Through Precision Agriculture: the Evolution of the Nebraska On-farm Research NetworkThe Nebraska On-Farm Research Network (NOFRN), allows farmers to answer production, profitability and sustainability questions in their own field. The University of Nebraska (USA) sponsors the NOFRN and provides technical support in the experimental design, execution, data analysis and results dissemination. In recent years, precision agriculture technologies have expanded network capabilities through an increasing ​number of experiments and provided new avenues for data analyses. The goal is... G. Balboa, B. Tobaldo, T. Lexow, J.D. Luck |