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The OLDS COLLEGE has issued a Tender notice for the procurement of a Machine Learning Support for Advancing Agronomy through Hyperlayer Data Collection and Analytics in the Canada. This Tender notice was published on 09 Feb 2022 and is scheduled to close on 21 Feb 2022, with an estimated Tender value of Refer Document. Interested bidders can access detailed Tender information, eligibility criteria, and complete bidding documents by referencing TOT Ref No. 62999686, while the tender notice number is RFP 125-2022 and Registering on the platform.
Procurement Summary
Country: Canada
Summary: Machine Learning Support for Advancing Agronomy through Hyperlayer Data Collection and Analytics
Deadline: 21 Feb 2022
Posting Date: 09 Feb 2022
Other Information
Notice Type: Tender
TOT Ref.No.: 62999686
Document Ref. No.: RFP 125-2022
Competition: ICB
Financier: Self Financed
Purchaser Ownership: Public
Tender Value: Refer Document
CPV Classification
16100000 - Agricultural and forestry machinery for soil preparation or cultivationPurchaser's Detail
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Login to see detailsTender Details
Description: N8720: Fertilizersu008p: End-To-End Learning Services (Excludes Cots Training)N3710: Soil Preparation Equipment5179h: General Machine Shop Servicesn3710t: Soil Preparation Equipment, Spare Partsaa910300: Agriculture (R&D)F006d: Crop Harvesting, not Elsewhere Specifiedn3710d: Fertilizer Spreaderf006e: Crop Services5179ha: General Machine Shop and Tool Room Servicesf006b: Harvesting Hay and Silageaa415504: Aquaculture (R&D)N3750r: Tractors, Gardening Response Submission: Olds College4500 50 Streetolds, Abpurchasing [email protected] Response Contact: Mosuro, Olatundesenior Procurement Coordinator4500 50 Stolds, Alberta T4h 1R6tel: 403-556-4787Fax: 1.403.556.4711Email: [email protected] Opportunity Description: the Overarching Goal of this Project is to Develop Predictive Algorithms Based on Machine Learning (Ml) for Estimating the Concentrations of Plant Available Nutrients in Soil, Instant Identification of Yield Limiting Factors, Calculating Best Achievable Yield Goals, and Automated Calculation of Site-Specific Fertilizer Prescriptions. Multiple Layers of Geospatial Data, such as Multi- and Hyperspectral Imagery, Thermal Imagery, Microwave Soil Moisture Measurements, Ground Penetrating Radar Data, High Density Soil Tests, Soil Compaction, Elevation Data and Field Surface Analysis, Gamma-Ray Soil Spectrometry Analysis, Soil Electrical Conductivity, and Yield Monitoring Data, will be Used to Build New Ml Predictive Algorithms, which w...
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