Proceedings
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| Filter results26 paper(s) found. |
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1. A Comparison of Plant Temperatures as Measured By Thermal Imaging and Infrared Thermometry... P. Baresel, B. Mistele, H. Yuncai, U. Schmidhalter, H. Hackl |
2. Applications for Precision Agriculture: the Italian Experience of SIRIUS ProjectThis paper reports the results of the project SIRIUS (Sustainable Irrigation water management and River-basin... P. Nino, S. Vanino, F. Lupia, F. Altobelli, F. Vuolo, I. Namdarian, C. De michele |
3. Remote Sensing Imagery Based Agricultural Land Pattern Extraction around Miyajimanuma WetlandThis research aimed to extract agricultural land use pattern around the Miyajimanuma wetland, Hokkaido, Japan. By combining the image segmentation technology - watershed transform and image classification technology- particle swarm optimization (PSO)-k-means based minimum distance classifier, a new method for extracting the agricultural land use information based... R. Mochizuki, I. Han-ya, N. Noguchi, B. Su, K. Ishii |
4. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial ImagesPotato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud |
5. Disease Scouting For Aerial Blight Based On Logical Areas Of Collection In Soybean Fields Rotated With RiceRhizoctonia solani AG1-IA causes sheath blight in rice and aerial blight in soybean. In Arkansas, rice and soybean rotations facilitate a continuous source of R. solani AG1-IA inoculum from one year to the next. Aerial blight is a two stage disease where colonization of the plant occurs during the early vegetative growth stages and aerial blight symptoms occur during the reproductive growth stages after canopy closure. At canopy closure,... C.S. Rothrock, W.S. Monfort, T.W. Griffin, T.N. Spurlock |
6. A Multi Sensor Data Fusion Approach for Creating Variable Depth Tillage Zones.Efficiency of tillage depends largely on the nature of the field, soil type, spatial distribution of soil properties and the correct setting of the tillage implement. However, current tillage practice is often implemented without full understanding of machine design and capability leading to lowered efficiency and further potential damage to the soil structure. By modifying the physical properties of soil only where the tillage is needed for optimum crop growth, variable depth tillage (VDT)... D. Whattoff, D. Mouazen, D. Waine |
7. Technology Support for Game Monitoring As a Tool for Damages Reduction of Field CropsWild boars (Sus scrofa) are increasingly becoming the main cause of field crops damage in Czech Republic and central Europe area. There are many reasons why wild boars population is growing. The major reason is most likely change in the composition of field crops. In some areas in particular there is focus on oilseed rape and maize, for which there are also recorded the biggest losses. One of the key discussion topics is the issue of estimation of animal quantities and its traceability.... J. Jarolimek, M. Stočes, M. Ulman, J. Vaněk |
8. Field Sampling and Electrochemical Detection of Nitrate in Agricultural SoilsNitrate is an essential plant nutrient and is added to farm fields to increase crop yields. While the addition of nitrate is important for production, over-fertilization with nitrate can lead to leaching and contamination of water bodies. Increased nitrate loading in water sources then leads to eutrophication and hypoxia in downstream regions. Many efforts are being made to accurately control nitrate fertilizer additions to fields. Here, we present a soil sampling device that directly samples... J. Brockgreitens, M. Bui, A. Abbas, D. Mulla |
9. Developing Nitrogen Algorithms for Corn Production Using Optical SensorsRemote sensing for nitrogen management in cereal crops has been an intensive research area due to environmental concerns and economic realities of today’s agronomic system. In the search for improved nitrogen rate decisions, what approach is most often taken and are those approaches justified through scientific investigation? The objective of this presentation is to educate decision makers on how these algorithms are developed and evaluate how well they work in the field on a small-plot... R.W. Mullen, S.B. Phillips, W.R. Raun, W.E. Thomason |
10. Canopy Temperature Mapping with a Vineyard RobotThe wine industry is a strategic sector in many countries worldwide. High revenues in the wine market typically result in higher investments in specialized equipment, so that producers can introduce disruptive technology for increasing grape production and quality. However, many European producers are approaching retirement age, and therefore the agricultural sector needs a way for attracting young farmers who can assure the smooth transition between generations; digital technology offers an opportunity... V. Saiz-rubio, M. Diago, J. Tardaguila, S. Gutierrez, F. Rovira-más, F. Alves |
11. 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 |
12. Reverse Modelling of Yield-Influencing Soil Variables in Case of Few Soil DataOur hypothesis was that simple models can be applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models can be used to test different sampling and interpolation approaches commonly applied in precision agriculture and to better predict soil variables at not observed locations. Three strategies for composite sample collection were compared in our study. Point samples were taken 1.) along lines within homogenous... I. Sisák, A. Benő, K. Szabó, M. Kocsis, J. Abonyi |
13. Influence of Planter Downforce Setting and Ground Speed on Seeding Depth and Plant Spacing Uniformity of CornUniform seed placement improves seed-to-soil contact and requires proper selection of downforce control across varying field conditions. At faster ground speeds, downforce changes and it becomes critical to select the level of planter downforce settings to achieve the desired consistency of seed placement during planting. The objective of this study was to assess the effect of ground speed and downforce setting on seeding depth and plant spacing and to evaluate the relationship of ground speed... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin |
14. Risk Efficiency of Site-Specific Nitrogen Management with Respect to Grain QualityProfitability analyses of site-specific nitrogen management strategies have often failed to provide reasons for adoption of precision farming implements. However, often effects of precision farming on product quality and price premiums were not taken into account. This study aims to evaluate comparative advantages of site-specific nitrogen management over uniform nitrogen management with respect to aspects of risk, considering fertilizer effects on grain quality and price premiums. We developed... A. Meyer-aurich, Y. Karatay, M. Gandorfer |
15. Experiences in the Development of Commercial Web-Based Data Engines to Support UK Growers Within an Industry-Academic PartnershipThe lifecycle of Precision Agriculture data begins the moment that the measurement is taken, after which it may pass through each multiple data processes until finally arriving as an output employed back in the production system. This flow can be hindered by the fact that many farm datasets have different spatial resolutions. This makes the process to aggregate or analyse multiple Precision Agriculture layers arduous and time consuming. Precision Decisions Ltd located in Yorkshire,... J. Taylor, Y. Shahar, P. James, C. Blacker, S. Leese, R. Sanderson, R. Kavanagh |
16. Economic Potential of RoboWeedMaps - Use of Deep Learning for Production of Weed Maps and Herbicide Application MapsIn Denmark, a new IPM ‘product chain’ has been constructed, which starts with systematic photographing of fields and ends up with field- or site-specific herbicide application. A special high-speed camera, mounted on an ATV took sufficiently good pictures of small weed plants, while driving up to 50 km/h. Pictures were uploaded to the RoboWeedMaps online platform, where appointed internal- and external persons with agro-botanical experience executed ‘virtual field inspection’... P. Rydahl, O. Boejer, N. Jensen, B. Hartmann, R. Jorgensen, M. Soerensen, P. Andersen, L. Paz, M.B. Nielsen |
17. Comparison of Canopy Extraction Methods from UAV Thermal Images for Temperature Mapping: a Case Study from a Peach OrchardCanopy extraction using thermal images significantly affects temperature mapping and crop water status estimation. This study aimed to compare several canopy extraction methodologies by utilizing a large database of UAV thermal images from a precision irrigation trial in a peach orchard. Canopy extraction using thermal images can be attained by purely statistical analysis (S), a combination of statistical and spatial analyses (SS), or by synchronizing thermal and RGB images, following RGB statistical... L. Katz, A. Ben-gal, I. Litaor, A. Naor, A. Peeters, E. Goldshtein, V. Alchanatis, Y. Cohen |
18. 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 resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
19. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS ReceiversGNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performed,... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic |
20. 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 |
21. Making Irrigator Pro an Adaptive Irrigation Decision Support SystemIrrigator Pro is a public domain irrigation scheduling model developed by the USDA-ARS National Peanut Research Laboratory. The latest version of the model uses either matric potential sensors to estimate the plant’s available soil water or manual data input. In this project, a new algorithm is developed, which will provide growers and consultants with much more flexibility in how they can feed data to the model. The new version will also run with Volumetric Water Content sensors, giving... I. Gallios, G. Vellidis, C. Butts |
22. Securing Agricultural Data with Encryption Algorithms on Embedded GPU Based Edge Computing DevicesSmart Agriculture (SA) has captured the interest of both the agricultural business and the scientific community in recent years. Overall, SA aims to help the agricultural and food industry to avoid crop failures, loss of revenues as well as help farmers use inputs (such as fertilizers and pesticides) more efficiently by utilizing Internet of Things (IoT) devices and computing systems. However, rapid digitization and reliance on data-driven technologies create new security threats that can defeat... M. Alahe, J.O. Kemeshi, Y. Chang, K. Won, X. Yang, M. Sher |
23. Yield Monitoring System for Radish and Cabbage Under Korean Field ConditionsYield monitoring is considered an essential tool to optimize resource utilization and provide an accurate assessment of crops for drylands. The objective of this study was to assess mass-based and volume-based yield monitoring under laboratory-simulated and field conditions for cabbage and radish. During the experiment, impact plate angles, conveyor speeds, and falling heights were systematically varied to investigate the effects on cabbage and radish yield during harvesting. Digital filtering... M. Gulandaz, M. Kabir, K. Shafik, S. Chung |
24. Determining Site-Specific Soybean Optimal Seeding Rate Using On-Farm Precision ExperimentationTen on-farm precision experiments were conducted in Nebraska during 2018 – 2022 to address the following: i) determine the Economic Optimal Seeding Rates (EOSR), ii) identify the most important site-specific variables influencing the optimal seeding rates for soybeans. Seeding rates ranged from 200,000 to 440,000 seeds ha-1, and treatments were randomized and replicated in blocks across the entire field. The study was implemented using a variable rate prescription. Yield... M.M. Dalla betta, L. Puntel, L. Thompson, T. Mieno, J.D. Luck, N. Cafaro la menza, P. Paccioretti |
25. Advancements in Agrivoltaics: Autonomous Robotic Mowing for Enhanced Management in Solar FarmsAgrivoltaics – the co-location of solar energy installations and agriculture beneath or between rows of photovoltaic panels – has gained prominence as a sustainable and efficient approach to land use. The US has over 2.8 GW in Agrivoltaics, integrating crop cultivation with solar energy. However, effective vegetation management is critical for solar panel efficiency. Flat, sunny agricultural land accommodates solar panels and crops efficiently. The challenge lies in managing grass... S. Behera, S. Pitla |
26. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management ZonesInvestment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed with... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe |