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Biswas, A
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Beeri, O
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Authors
Adamchuk, V.I
Dhawale, N
Biswas, A
Lauzon‎, S
Dutilleul, P
Cambouris, A
Lajili, A
Chokmani , K
Perron, I
Adamchuk, V
Biswas , A
Zebrath, B
Beeri, O
Pelta, R
Mey-tal, S
Raz, J
Mills, B
Brorsen, B
Arnall, D
Rud, R
Beeri, O
Mey-tal , S
Biswas, A
Ji, W
Perron, I
Cambouris, A
Zebarth, B
Adamchuk, V
Biswas, A
Vidana Gamage, D.N
Strachan, I.B
Beeri, O
May-tal, S
Rud, R
Raz, Y
Pelta, R
Beeri, O
May-tal, S
Raz, J
Rud, R
Cambouris, A
Perron, I
Zebarth, B
Vargas, F
Chokmani, K
Biswas, A
Adamchuk, V
Johnston, A
Adamchuk, V
Biswas, A
Cambouris, A
Lafond, J
Perron, I
Saifuzzaman, M
Adamchuk, V.I
Huang, H
Ji, W
Rabe, N
Biswas, A
Huang, H
Adamchuk, V
Biswas, A
Ji, W
Lauzon, S
Pelta, R
Beeri, O
Shilo, T
Tarshish, R
Saurette, D
Biswas, A
Gobezie, T.B
Beeri, O
Pelta, R
Sade, Z
Shilo, T
Fathololoumi, S
Firozjaei, M.K
Biswas, A
Daggupati, P
Hoffmann Silva Karp, F
Adamchuk, V
Dutilleul, P
Melnitchouck, A
Biswas, A
Topics
Big Data Mining & Statistical Issues in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Site-Specific Nutrient, Lime and Seed Management
Drainage Optimization and Variable Rate Irrigation
Decision Support Systems
Big Data, Data Mining and Deep Learning
Geospatial Data
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Precision Horticulture
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Type
Oral
Poster
Year
2016
2018
2022
2024
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Filter results18 paper(s) found.

1. Integrated Analysis of Multilayer Proximal Soil Sensing Data

Data revealing spatial soil heterogeneity can be obtained in an economically feasible manner using on-the-go proximal soil sensing (PSS) platforms. Gathered georeferenced measurements demonstrate changes related to physical and chemical soil attributes across an agricultural field. However, since many PSS measurements are affected by multiple soil properties to different degrees, it is important to assess soil heterogeneity using a multilayer approach. Thus, analysis of multiple layers of geospatial... V.I. Adamchuk, N. Dhawale, A. Biswas, S. Lauzon‎, P. Dutilleul

2. Use of Proximal Soil Sensing to Delineate Management Zones in a Commercial Potato Field in Prince Edward Island, Canada

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties. Such MZs can often be used for site-specific management of crop production inputs. The purpose of this study was to determine the efficiency of two proximal soil sensors for delineating MZs in an 8.1-ha commercial potato (Solanum tuberosum L.) field in Prince Edward Island (PEI), Canada. A galvanic contact resistivity sensor (Veris-3100 [Veris]) and electromagnetic induction sensors... A. Cambouris, A. Lajili, K. Chokmani , I. Perron, V. Adamchuk, A. Biswas , B. Zebrath

3. Data Fusion of Imagery from Different Satellites for Global and Daily Crop Monitoring

Satellite-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

4. The Profitability of Variable Rate Lime in Wheat

Grid sampling allows a variable rate of lime to be applied and has been marketed as a cost saver to producers. However, there is little research that shows if this precision application is profitable or not. Previous research on variable-rate lime has considered only a small number of fields. This paper uses soil sampling data from 170 fields provided by producers in Oklahoma and Kansas. We compare net returns of variable rate to uniform rate lime for grain only wheat production, dual-purpose... B. Mills, B. Brorsen, D. Arnall

5. Designated Value for a Field Polygon Based on Imagery Data: A Case Study of Crop Vigor in Agricultural Application for Irrigation

Any irrigation action for a field management zone, which is based on images, requires a transformation into single value. Since data distribution is ab-normal in an image, using a mean value to estimate the crop coefficient (Kc), an overlaid polygon may not represent properly its water demand. Therefore, this project’s aim was to examine to which extent different statistics of potential designated values will affect an estimated Kc, and consequently affect irrigation practices. Satellite... R. Rud, O. Beeri, S. Mey-tal

6. Proximal Soil Sensing-Led Management Zone Delineation for Potato Fields

A fundamental aspect of precision agriculture or site-specific crop management is the ability to recognize and address local changes in the crop production environment (e.g. soil) within the boundaries of a traditional management unit. However, the status quo approach to define local fertilizer need relies on systematic soil sampling followed by time and labour-intensive laboratory analysis. Proximal soil sensing offers numerous advantages over conventional soil characterization and has shown... A. Biswas, W. Ji, I. Perron, A. Cambouris, B. Zebarth, V. Adamchuk

7. High Resolution Soil Moisture Monitoring Using Active Heat Pulse Method with Fiber Optic Temperature Sensing at Field Scale

Knowledge of spatial and temporal variability of soil moisture is critical for site specific irrigation management at field scale. However, installation feasibility, cost and between-sensor variability restrict the use of many point–based sensors at field scale. Active heat pulse method with fiber optic temperature sensing (AHFO) has shown a potential to provide soil moisture data at sub-meter intervals along a fiber optic cable to a distance >10000 meters. Despite the limited number... A. Biswas, D.N. Vidana gamage, I.B. Strachan

8. Detecting Variability in Plant Water Potential with Multi-Spectral Satellite Imagery

Irrigation Intelligence is a practice of precise irrigation, with the goal of providing crops with the right amount of water, at the right time, for optimized yield. One of the ways to achieve that, on a global scale, is to utilize Landsat-8 and Sentinel-2 images, providing together frequent revisit cycles of less than a week, and an adequate resolution for detection of 1 ha plots. Yet, in order to benefit from these advantages, it is necessary to examine the information that can be extracted... O. Beeri, S. May-tal, R. Rud, Y. Raz, R. Pelta

9. Field Test of a Satellite-Based Model for Irrigation Scheduling in Cotton

Cotton 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

10. Delineation of Soil Management Zones: Comparison of Three Proximal Soil Sensor Systems Under Commercial Potato Field in Eastern Canada.

Precision agriculture (PA) involves optimization of seeding, fertilizer application, irrigation, and pesticide use to optimize crop production for the purpose of increasing grower revenue and protecting the environment. Potato crops (Solanum tuberosum L.) are recognized as good candidates for the adoption of PA because of the high cost of inputs. In addition, the sensitivity of potato yield and quality to crop management and environmental conditions makes precision management economically... A. Cambouris, I. Perron, B. Zebarth, F. Vargas, K. Chokmani, A. Biswas, V. Adamchuk

11. Integration of Proximal and Remote Sensing Data for Site-Specific Management of Wild Blueberry

In Saguenay-Lac-St-Jean, there are nearly 27,000 ha of wild blueberries (Vaccinium angustifolium Ait.). This production is carried out in fields with heterogeneous growing conditions due to the local changes in topography, key soil properties, and crop density. The main objective of this study was to develop a regression-based approach to site-specific management (SSM) by integrating proximally and remotely sensed data layers, namely, apparent soil electrical conductivity (ECa), field elevation,... A. Johnston, V. Adamchuk, A. Biswas, A. Cambouris, J. Lafond, I. Perron

12. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing Data

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively homogeneous... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas

13. Analysis of Soil Properties Predictability Using Different On-the-Go Soil Mapping Systems

Understanding the spatial variability of soil chemical and physical attributes allows for the optimization of the profitability of nutrient and water management for crop development. Considering the advantages and accessibility of various types of multi-sensor platforms capable of acquiring large sensing data pertaining to soil information across a landscape, this study compares data obtained using four common soil mapping systems: 1) topography obtained using a real-time kinematic (RTK) global... H. Huang, V. Adamchuk, A. Biswas, W. Ji, S. Lauzon

14. A Hyperlocal Machine Learning Approach to Estimate NDVI from SAR Images for Agricultural Fields

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture used globally since the 1970s. The NDVI is sensitive to the biochemical and physiological properties of the crop and is based on the Red (~650 nm) and NIR (~850 nm) spectral bands. It is used as a proxy to monitor crop growth, correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions which might alter... R. Pelta, O. Beeri, T. Shilo, R. Tarshish

15. Digital Soil Sensing and Mapping for Crop Suitability

Soil, central to any land-based production system, determines the success of any crops. While soil for a farm or field is fixed, the crops can be selected to best fit the soil’s capability and production. Traditionally crops are selected based on farm history, knowledge, and years of trial and error to tailor the right crop to the right soil. Inherent challenges associated with this make the whole process unsustainable. Due to the consistent nature of the information collected, soil sensors... D. Saurette, A. Biswas, T.B. Gobezie

16. Multi-sensor Imagery Fusion for Pixel-by-pixel Water Stress Mapping

Evaluating water stress in agricultural fields is fundamental in irrigation decision-making, especially mapping the in-field water stress variability as it allows real-time detection of system failures or avoiding yield loss in cases of unplanned water stress. Water stress mapping by remote sensing imagery is commonly associated with the thermal or the short-wave-infra-red (SWIR) bands. However, integration of multi-sensors imagery such as radar imagery or sensors with only visible and near-infra-red... O. Beeri, R. Pelta, Z. Sade, T. Shilo

17. A Fusion Strategy to Map Corn Crop Residues

Access to post-harvest residue coverage information is crucial for agricultural management and soil conservation. The purpose of this study was to present a new approach based on an ensemble at the decision level for mapping the corn residue. To this end, a set of Landsat 8 imagery and field data including the Residue Cover Fraction (RCF) of corn (149 samples), were used. Firstly, a map of common spectral indices for RCF modeling was prepared based on the spectral bands. Then, the efficiency of... S. Fathololoumi, M.K. Firozjaei, A. Biswas, P. Daggupati

18. Predicting Soil Chemical Properties Using Proximal Soil Sensing Technologies and Topography Data: a Case Study

Using proximal soil sensors (PSS) is widely recognized as a strategy to improve the quality of agricultural soil maps. Nevertheless, the signals captured by PSS are complex and usually relate to a combination of processes in the soil. Consequently, there is a need to explore further the interactions at the source of the information provided by PSS. The objectives of this study were to examine the relationship between proximal sensing techniques and soil properties and evaluate the feasibility... F. Hoffmann silva karp, V. Adamchuk, P. Dutilleul, A. Melnitchouck, A. Biswas