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Swain, D
Arnold, S
De Poorter, E
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Authors
Trotter, M
Gregory, S
Trotter, T
Acuna, T
Swain, D
Fasso, W
Roberts, J
Zikan, A
Cosby, A.M
De Waele, T
Peralta, D
Shahid, A
De Poorter, E
Javed, B
Cambouris, A
Duchemin, M
Longchamps, L
Basran, P.S
Arnold, S
Fenech, A
Karam, A
Topics
Agricultural Education
Big Data, Data Mining and Deep Learning
Precision Agriculture and Global Food Security
Type
Oral
Poster
Year
2016
2022
2024
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1. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural Education

The industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby

2. Supervised Feature Selection and Clustering for Equine Activity Recognition

In this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a mean... T. De Waele, D. Peralta, A. Shahid, E. De Poorter

3. 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