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Farooque, A
Alahe, M
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Authors
Esau, K
Zaman, Q
Farooque, A
Schumann, A
Mohamed, M.M
Zaman, Q
Esau, T
Farooque, A
Ali, U
Esau, T.J
Farooque, A
Zaman, Q
Khan, H
Esau, T
Farooque, A
Abbas, F
Kemeshi, J.O
Chang, Y
Yadav, P.K
Alahe, M
Alahe, M
Kemeshi, J.O
Chang, Y
Won, K
Yang, X
Sher, M
Alahe, M
Chang, Y
Kemeshi, J.O
Gummi, S
Menendez III, H
Alahe, M
Gummi, S
Kemeshi, J.O
Chang, Y
Gummi, S
Alahe, M
Chang, Y
Pack, C
Zaman, Q.U
Farooque, A
Jamei, M
Esau, T.J
Topics
Decision Support Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Decision Support Systems
Precision Agriculture and Global Food Security
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Edge Computing and Cloud Solutions
Site-Specific Pasture Management
Precision Agriculture and Global Food Security
Robotics and Automation with Row and Horticultural Crops
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2018
2022
2024
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Filter results10 paper(s) found.

1. 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

2. Design of Ground Surface Sensing Using RADAR

Ground sensing is the key task in harvesting head control system. Real time sensing of field topography under vegetation canopy is very challenging task in wild blueberry cropping system. This paper presents the design of an ultra-wide band RADAR sensing, scanning device to recognize the soil surface level under the canopy structure. Requirements for software and hardware were considered to determine the usability of the ultra-wide band RADAR system.An automated head elevation... M.M. Mohamed, Q. Zaman, T. Esau, A. Farooque

3. Integration of High Resolution Multitemporal Satellite Imagery for Improving Agricultural Crop Classification: a Case Study

Timely and accurate agriculture information is vital for ensuring global food security. Satellite imagery has already been proved as a reliable tool for remote crop mapping. Planet satellite imagery provides high cadence, global satellite coverage with higher temporal and spatial resolution than the Landsat-8 and Sentinel-2. This study examined the potential of utilizing high-resolution multitemporal imagery along with and normalized difference vegetation index (NDVI) to map the agricultural crops... U. Ali, T. Esau, A. Farooque, Q. Zaman

4. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting Losses

The production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada were... H. Khan, T. Esau, A. Farooque, F. Abbas

5. Comparing Global Shutter and Rolling Shutter Cameras for Image Data Collection in Motion on a UGV

In a bid to drive the adoption of precision farming (PF) technology by reducing the cost of developing an Unmanned Ground Vehicle (UGV), during the Reduction-To-Below-Two grand (R2B2) project we compared Arducam’s AR0234, a global shutter camera (GSC) to their IMX462, a rolling shutter camera (RSC). Since the cost of the AR0234 is approximately three times the price of the IMX462, the comparison was done to determine the possibility of using the latter for image data collection in place... J.O. Kemeshi, Y. Chang, P.K. Yadav, M. Alahe

6. Securing Agricultural Data with Encryption Algorithms on Embedded GPU Based Edge Computing Devices

Smart 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

7. Design of an Automatic Travelling Electric Fence System for Sustainable Grazing Management

Fences are used in Precision Livestock Farming (PLF) to prevent herbivores from overgrazing and under grazing forages. While effective in controlling animal entry and exit, traditional fences are not flexible enough to meet the needs of both foraging animals and plants in terms of both nutrient availability and physiological demands. An electric fencing system is a form of traditional fencing that employs an electric charge to create a barrier and dissuade animals or people from crossing it. Even... M. Alahe, Y. Chang, J.O. Kemeshi, S. Gummi, H. Menendez iii

8. Securing Agricultural Imaging Data in Smart Agriculture: a Blockchain-based Approach to Mitigate Cybersecurity Threats and Future Innovations

Smart agriculture (SA) is a new technology that combines the Internet of Things (IoT) with a variety of smart devices, such as drones, unmanned ground vehicles (UGVs), and computer systems. The integration of technology improvements in SA has led to an increase in cybersecurity concerns, specifically pertaining to the protection of sensitive agricultural image data. It’s necessary to better understand SA network systems; establish stronger network structures; identify different types and... M. Alahe, S. Gummi, J.O. Kemeshi, Y. Chang

9. Voronoi-based Ant Colony Optimization Approach: Autonomous Robotic Swarm Navigation for Crop Disease Detection

The early detection of agricultural diseases is essential for sustaining food production and economic viability over the long term. To improve disease detection in agriculture, this paper presents an innovative computational approach that utilizes the Voronoi-based Ant Colony Optimization (V-ACO) algorithm with Swarm Robotics (SR). Inspired by the social behaviors observed in insect colonies such as honeybees and ants, SR offers new opportunities for precision farming. SR utilizes the coordinated... S. Gummi, M. Alahe, Y. Chang, C. Pack

10. Application of Advanced Soft Computing to Estimate Potato Tuber Yield: a Case Study from Atlantic Canada

The potato crop plays a crucial role in the economy of Atlantic Canada, particularly in Prince Edward Island and New Brunswick, where it contributes significantly to potato production. To help farmers make informed decisions for sustainable and profitable farming, this study was conducted to examine the variations in potato tuber yield based on thirty soil properties collected over four growing seasons through experimental trials. The study employed an advanced and explainable ensemble model called... Q.U. Zaman, A. Farooque, M. Jamei, T.J. Esau