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Esau, K
Vieri, M.P
Cambouris, A.N
O'Sullivan, N
Oliveira, W.K
Gowda, H.H
Anaba, C.I
Carter, .R
Erickson, B.J
Alves de Lima, J.
Odvody, G.N
Canata, T.F
Albrigo, L.G
Orellana, M.C
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Authors
Gowda, H.H
Reddy, K.A
Patil, M.B
L, R.N
Shanwad, U
Yang, C
Odvody, G.N
Fernandez, C.J
Landivar, J.A
Nichols, R.L
Lee, W
Kumar, A
Ehsani, R
Yang, C
Albrigo, L.G
Pagni, P
Ghinassi, G.P
Vieri, M.P
Yang, C
Odvody, G.N
Minzenmayer, R.R
Nichols, R.L
Isakeit, T
Thomasson, A
Martello, L.S
Canata, T.F
Sousa, R.V
Maldaner, L
Molin, J.P
Canata, T.F
Canata, T.F
Molin, J.P
Colaço, A.F
Trevisan, R.G
Fiorio, P.R
Martello, M
Esau, K
Zaman, Q
Farooque, A
Schumann, A
Erickson, B.J
Lowenberg-DeBoer, J
Li, D
Miao, Y
Fernández, .G
Kitchen, N.R
Ransom, C.
Bean, G.M
Sawyer, .E
Camberato, J.J
Carter, .R
Ferguson, R.B
Franzen, D.W
Franzen, D.W
Franzen, D.W
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Shanahan, J.F
Nze Memiaghe, J
Cambouris, A.N
Ziadi, N
Duchemin, M
Karam, A
Ortega, R.A
Ortega, A.F
Orellana, M.C
Anaba, C.I
Dhal, S
Louis, J
O'Sullivan, N
Gumero, J
Soetan, M
Kalafatis, S
Lusher, J
Mahanta, S
Bazzi, C.L
Rauber, L.A
Oliveira, W.K
Sobjak, R
Schenatto, K
Gebler, L
Rabello, L.M
Bazzi, C.L
Oliveira, W.K
Sobjak, R
Schenatto, K
Souza, E
Hachisuca, A
Franz, F
Avila, E.N
Bazzi, C.L
Oliveira, W.K
Schenatto, K
Sobjak, R
Rocha, D.M
Sobjak, R
Bazzi, C.L
Schenatto, K
Oliveira, W.K
Menegasso, A.E
Da Silva, M.L
Alves de Lima, J.
Balbinot, A
Molin, J.P
Topics
Spatial Variability in Crop, Soil and Natural Resources
Machine Vision / Multispectral & Hyperspectral Imaging Applications to Precision Agriculture
Precision Horticulture
Sensor Application in Managing In-season Crop Variability
Precision Crop Protection
Precision Dairy and Livestock Management
Spatial Variability in Crop, Soil and Natural Resources
Remote Sensing Applications in Precision Agriculture
Decision Support Systems
Factors Driving Adoption
ISPA Community: Nitrogen
Decision Support Systems
Land Improvement and Conservation Practices
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Wireless Sensor Networks and Farm Connectivity
Big Data, Data Mining and Deep Learning
Artificial Intelligence (AI) in Agriculture
Data Analytics for Production Ag
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
2024
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Filter results20 paper(s) found.

1. 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

2. Evaluating Spectral Measures Derived From Airborne Multispectral Imagery for Detecting Cotton Root Rot

Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is one of the most destructive plant diseases occurring... C. Yang, G.N. Odvody, C.J. Fernandez, J.A. Landivar, R.L. Nichols

3. Citrus Greening Disease Detection Using Airborne Multispectral And Hyperspectral Imaging

Citrus greening disease (Huanglongbing or HLB) has become a major catastrophic disease in Florida’s $9 billion citrus industry since 2005, and continued to be spread to other parts of the U.S. There is no known cure for this disease. As of October 2009, citrus trees in 2,702 different sections (square mile) in 34 counties were infected in Florida. A set of hyperspectral imageries were used to develop disease detection algorithms using image-derived spectral library, the mixture tuned... W. Lee, A. Kumar, R. Ehsani, C. Yang, L.G. Albrigo,

4. Optimizing Vineyard Irrigation Through The Automatic Resistivity Profiling (arp) Technology. The Proposal Of A Methodological Approach

 In Tuscany, central Italy, grape cultivation and wine production (i.e., Chianti DOCG, Brunello di Montalcino) are farming activities appreciated worldwide. Differently from the past, irrigation is allowed to meet the intense physiological stress that may occur during seasons affected by the increasing climate variability, in order to guarantee quality product and hence high market profitability in many vines areas. Most vineyards... P. Pagni, G.P. Ghinassi, M.P. Vieri

5. Using Airborne Imagery To Monitor Cotton Root Rot Infection Before And After Fungicide Treatment

Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is a severe soilborne disease that has affected cotton production for over a century. Recent research has shown that a commercial fungicide, flutriafol, has potential for the control of this disease. To effectively and economically control this disease, it is necessary to identify infected areas within the field so that variable rate technology can be used to apply fungicide only to the... C. Yang, G.N. Odvody, R.R. Minzenmayer, R.L. Nichols, T. Isakeit, A. Thomasson

6. Application Of Infrared Thermography For Assessing Beef Cattle Comfort Using A Fuzzy Logic Classifier

... L.S. Martello, T.F. Canata, R.V. Sousa

7. Processing Yield Data from Two or More Combines

Erroneous 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

8. 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, previously... T.F. Canata, J.P. Molin, A.F. Colaço, R.G. Trevisan, P.R. Fiorio, M. Martello

9. Effective Use of a Debris Cleaning Brush for Mechanical Wild Blueberry Harvesting

Wild blueberries are an important horticultural crop native to northeastern North America. Management of wild blueberry fields has improved over the past decade causing increased plant density and leaf foliage. The majority of wild blueberry fields are picked mechanically using tractor mounted harvesters with 16 rotating rakes that gently comb through the plants. The extra foliage has made it more difficult for the cleaning brush to remove unwanted debris (leaf, stems, weeds, etc.) from the picker... K. Esau, Q. Zaman, A. Farooque, A. Schumann

10. Survey Shows Specialty and Commodity Crop Retailers Use Precision Agriculture Differently

The 2021 CropLife-Purdue Survey of precision agricultural practices by US agricultural input dealers serving the American grain and oilseed sector shows that most of them use GPS guidance and related technologies like sprayer boom control, most provide variable rate fertilizer services, and the majority say that fertilizer decisions are influenced by grower data. In contrast, dealers serving horticultural and specialty crop farms indicate comparatively modest adoption of many precision agriculture... B.J. Erickson, J. Lowenberg-deboer

11. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest

Effective 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

12. Impacts of Interpolating Methods on Soil Agri-environmental Phosphorus Maps Under Corn Production

Phosphorus (P) is an essential nutrient for crops production including corn. However, the excessive P application, tends to P accumulation at the soil surface under crops systems. This may contribute to increase water and groundwater pollution by surface runoff. To prevent this, an agri-environmental P index, (P/Al)M3, was developed in Eastern Canada and USA. This index aims to estimate soil P saturation for accurate P fertilizer recommendations, while integrating agronomical aspects... J. Nze memiaghe, A.N. Cambouris, N. Ziadi, M. Duchemin, A. Karam

13. Analysis of the Mapping Results Using SoilOptix TM Technology in Chile After Two Seasons

Soil mapping is a key element to successfully implement Integrated Nutrient Management (INM) in high value crops.  SoilOptixTM is a mapping service based on the use of gamma radiation technology that arrived in Chile in 2019. Since then, around 2000 ha have been mapped, mainly in fruit orchards and vineyards. The technology has demonstrated its value in determining the most limiting factors in new and old orchards, and the possibility of correcting them in a site-specific... R.A. Ortega, A.F. Ortega, M.C. Orellana

14. Farmers’ and Experts’ Perceptions of Precision Farming Impacts on Economic Efficiency, Food Security, Climate and Environmental Sustainability

“Global food security could be in jeopardy, due to mounting pressures on natural resources and to climate change, both of which threaten the sustainability of food systems at large. Excessive fertilizer use can contribute to problems of eutrophication, acidification, climate change and the toxic contamination of soil, water and air. Lack of fertilizer application may cause the degradation of soil fertility. Agricultural production systems need to focus more on the effective conservation... C.I. Anaba

15. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-up

The 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

16. Portable Soil EC - Development of an Electronic Device for Determining Soil Electrical Conductivity

Decision-making in agriculture demands continuous monitoring, a factor that propels the advancement of tools within Agriculture 4.0. In this context, understanding soil characteristics is essential. Electrical conductivity (EC) sensors play a pivotal role in this comprehension. Given this backdrop, the core motivation of this research was developing an accessible and effective electronic device to measure the apparent EC of the soil. It provides features like geolocation, recording of the date... C.L. Bazzi, L.A. Rauber, W.K. Oliveira, R. Sobjak, K. Schenatto, L. Gebler, L.M. Rabello

17. AgDataBox-IoT - Managing IoT Data and Devices on Precision Agriculture

The increasing global population has resulted in a substantial demand for nourishment, which has prompted the agricultural sector to investigate ways to improve efficiency. Precision agriculture (PA) uses advanced technologies such as the Internet of Things (IoT) and sensor networks to collect and analyze field information. Although the advantages are numerous, the available data storage, management, and analysis resources are limited. Therefore, creating and providing a user-friendly web application... C.L. Bazzi, W.K. Oliveira, R. Sobjak, K. Schenatto, E. Souza, A. Hachisuca, F. Franz

18. Geographic Database in Precision Agriculture for the Development of AI Research

Agriculture 4.0 has profoundly transformed production processes by incorporating technologies such as Precision Agriculture, Artificial Intelligence, the Internet of Things, and telemetry. This evolution has enabled more accurate and timely decision-making in agriculture. In response to this movement, the Precision Agriculture Laboratory (AgriLab) of UTFPR, located in Medianeira, proposes the establishment of a consistent and standardized database. This database is continually updated with surveys... E.N. Avila, C.L. Bazzi, W.K. Oliveira, K. Schenatto, R. Sobjak, D.M. Rocha

19. AgDataBox-IA – Web Application with Artificial Intelligence for Agricultural Data Analysis in Precision Agriculture

Agriculture has been continually evolving, incorporating hardware, software, sensors, aerial surveys, soil sampling for chemical, physical, and granulometric analysis (based on sample grids), and microclimatic data, leading to a substantial volume of data. This requires platforms to store, manage, and transform these data into actionable information for decision-making in the field. In this regard, Artificial Intelligence (AI) is the most widely used tool globally to mine and transform vast data... R. Sobjak, C.L. Bazzi, K. Schenatto, W.K. Oliveira, A.E. Menegasso

20. Yield Analysis in Sugarcane Harvesters Using Design of Experiments (DoE) Methodology

The sugarcane crop is highlighted in national agribusiness, Brazil is the world’s largest producer of the plant, and the prospection of specialists is of strong growth for the next years. However, in order to increase productivity, technological interventions through of precision agriculture must be implemented. Among them, the management of inputs guided by yield spatial variability for otmizing production and income. This project approaches the implementation of the methodology of analysis... M.L. Da silva, J. . Alves de lima, A. Balbinot, J.P. Molin