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Azam, S
Ahmad, A
Archila-Diaz, J.F
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Authors
Liaghat, S
Mansor, S
Shafri, H
Meon, S
Ehsani, R
Azam, S
Noh, N
Archila-Diaz, J.F
Becker, M
Ahmad, A
Aggarwal, V
Saraswat, D
El Gamal, A
Johal, G
Topics
Precision Horticulture
Engineering Technologies and Advances
Applications of Unmanned Aerial Systems
Type
Poster
Oral
Year
2012
2016
2022
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Filter results3 paper(s) found.

1. Early Detection of Oil Palm Fungal Disease Infestation Using A Mid-Infrared Spectroscopy Technique

Basal stem rot (BSR) caused by Ganoderma boninense is known as the most destructive disease of oil palm plantations in Southeast Asia. Ganoderma could potentially reduce the market share of palm oil for Malaysia. Currently Malaysia produces about 50% of the world’s supply of palm oil. Early, accurate, and non-destructive diagnosis of Ganoderma fungal infection is critical for management of this disease. Early disease management of Ganoderma could also prevent great losses in production and... S. Liaghat, S. Mansor, H. Shafri, S. Meon, R. Ehsani, S. Azam, N. Noh

2. Simulation of Curiosity and Exo Mars Rovers on Agriculture Terrain

Improving agricultural productivity is one of the biggest challenges Agriculture and Engineering face. A possible solution is the creation of soil databases and/or maps to apply precision agriculture techniques, aiming to produce more in the same land, using less agricultural supplies. This practice may be developed with the help of rovers applied to e.g. agricultural data collect, mapping, scouting and supply tasks. However, the rover needs to move and adapt to the terrain to obtain a real appropriate... J.F. Archila-diaz, M. Becker

3. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal