Login

Proceedings

Find matching any: Reset
Vigil, M
Manfrini, L
Mouazen, D
Add filter to result:
Authors
Whattoff, D
Mouazen, D
Waine, D
Walthall, C
Hatfield, J
Schneider, S
Vigil, M
Bresilla, K
Manfrini, L
Boini, A
Perulli, G
Morandi, B
Grappadelli, L.C
Topics
Proximal Sensing in Precision Agriculture
Decision Support Systems in Precision Agriculture
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2016
2018
Home » Authors » Results

Authors

Filter results3 paper(s) found.

1. A Multi Sensor Data Fusion Approach for Creating Variable Depth Tillage Zones.

Efficiency of tillage depends largely on the nature of the field, soil type, spatial distribution of soil properties and the correct setting of the tillage implement.  However, current tillage practice is often implemented without full understanding of machine design and capability leading to lowered efficiency and further potential damage to the soil structure. By modifying the physical properties of soil only where the tillage is needed for optimum crop growth, variable depth tillage (VDT)... D. Whattoff, D. Mouazen, D. Waine

2. Closing Yield Gaps with GxExM and Precision Agriculture

There are many challenges to be faced by agriculture if the global population of nine billion people projected for 2050 is to be fed and clothed, especially given the effects of changing climate.  A focus on the interactions of genetics x environment x management (GxExM) offers potential for meeting the yield, and environment and economic sustainability goals that are integral to these challenges.  The yield gap –defined as the difference between current farmer yields and potential... C. Walthall, J. Hatfield, S. Schneider, M. Vigil

3. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the Tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli