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Fritz, A
Anderson, L
Duchemin, M
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Authors
Caron, J
Anderson, L
Sauvageau, G
Gendron, L
Evers, B
Rekhi, M
Hettiarachchi, G
Welch, S
Fritz, A
Alderman, P.D
Poland, J
Nze Memiaghe, J
Cambouris, A.N
Ziadi, N
Duchemin, M
Karam, A
Cambouris, A
Duchemin, M
Ziadi, N
Javed, B
Cambouris, A
Duchemin, M
Longchamps, L
Basran, P.S
Arnold, S
Fenech, A
Karam, A
Cambouris, A
Duchemin, M
Lord, E
Ziadi, N
Javed, B
Nze Memiaghe, J.D
Ramirez-Gonzalez, D.A
Nze Memiaghe, J
Cambouris, A
Duchemin, M
Ziadi, N
Karam, A
Cambouris, A
Lord, E
Duchemin, M
Topics
Precision Horticulture
Geospatial Data
Decision Support Systems
In-Season Nitrogen Management
Precision Agriculture and Global Food Security
On Farm Experimentation with Site-Specific Technologies
Land Improvement and Conservation Practices
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Oral
Poster
Year
2018
2022
2024
2026
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Authors

Filter results8 paper(s) found.

1. Real Time Precision Irrigation with Variable Setpoint for Strawberry to Generate Water Savings

Water is a precious resource that is becoming increasingly scarce as the population grows and water resources are depleted in some locations or under increased control elsewhere, due to local availability or groundwater contamination issues. It obviously affects strawberry (Fragaria x ananassa Duch.) production in populated areas and water cuts are being imposed to many strawberry growers to save water, with limited information on the impact on crop yield. Precision irrigation technologies are... J. Caron, L. Anderson, G. Sauvageau, L. Gendron

2. Using On-the-Go Soil Sensors to Assess Spatial Variability within the KS Wheat Breeding Program

In plant breeding the impacts of genotype by environment interactions and the challenges to quantify these interactions has long been recognized. Both macro and microenvironment variations in precipitation, temperature and soil nutrient availability have been shown to impact breeder selections. Traditionally, breeders mitigate these interactions by evaluating genotype performance across varying environments over multiple years. However, limitations in labor, equipment and seed availably can limit... B. Evers, M. Rekhi, G. Hettiarachchi, S. Welch, A. Fritz, P.D. Alderman, J. Poland

3. 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

4. Nitrogen Fertilization of Potato Using Management Zone in Prince Edward Island, Canada

Potato is sensible to nitrogen (N) and optimal N fertilization improve the tuber yield and its quality. Potato crop N response varies widely within fields. It is also well recognized that significant spatial and temporal variation in soil N availability occurs within crop fields. However, uniform application of N fertilizer is still the most common practice under potato production. Management zone (MZ) approach can help growers to achieve a part of this. The goal of the project is to compare the... A. Cambouris, M. Duchemin, N. Ziadi

5. In-season Nitrogen Prediction Evaluation Using Airborne Imagery with AI Techniques in Commercial Potato Production

In modern agriculture, timely and precise nitrogen (N) monitoring is essential to optimize resource management and improve trade benefits. Potato (Solanum tuberosum L.) is a staple food in many regions of the world, and improving its production is inevitable to ensure food security and promote related industries. Traditional methods of assessing nitrogen are labour-intensive, time-consuming, and require subjective observations. To address these limitations, a combination of multispectral... B. Javed, A. Cambouris, M. Duchemin, L. Longchamps, P.S. Basran, S. Arnold, A. Fenech, A. Karam

6. Assessment of Soil Spatial Properties and Variability Using a Portable VIS-NIRS Soil Probe for On-farm Precision Experimentation

Assessing the spatial variability of soil properties represents an important issue for on-farm sustainable management owing to high cost of sampling densities. Actual methods of soil properties measurement are based on conventional soil sampling of one sample per ha, followed by laboratory analysis, requiring many soil extraction processes and harmful chemicals. This conventional laboratory analysis does not allow exploring spatial variation of soil properties at desired fine spatial scale. Thus,... A. Cambouris, M. Duchemin, E. Lord, N. Ziadi, B. Javed, J.D. Nze Memiaghe, D.A. Ramirez-gonzalez

7. Delineating Management Zones for Optimizing Soil Phosphorus Recommendations Under a No Till Field in Eastern Canada

Corn (Zea mays L.) and soybean (Glycine max L.) represent the most common crop rotation in Eastern Canada. These crops are cultivated using no-tillage (NT) practice to enhance agroecosystem sustainability. However, NT practice can cause several agri-environmental issues related to phosphorus (P) stratification, movement and runoff leading to P eutrophication in waters. Another major challenge is the expensive costs of extensive soil sampling and laboratory tests needed for accurate... J. Nze Memiaghe, A. Cambouris, M. Duchemin, N. Ziadi, A. Karam

8. Enhancing the Reliability of Portable Soil Probes Through Machine Learning Optimization

Precision agriculture requires high resolution soil data, yet traditional laboratory analyses limit sampling because of high human labour and analysis costs. Portable spectrographic tools allow rapid infield soil characterization with multiple measurements, although their accuracy often falls short of standard laboratory protocols. We hypothesized that machine learning (ML) models could improve the reliability of these tools by adjusting their outputs against laboratory reference values. A... A. Cambouris, E. Lord, M. Duchemin