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Gray, G.R
Gianello, E
Gore, A.K
Grappadelli, L.C
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Authors
Mueller, T
Gianello, E
Mijatovic, B
Rienzi, E
Rodrigues, M
Pawar, S.N
Gore, A.K
Shinde, G.U
Pendke, M.S
Cerri, D.G
Gray, G.R
Magalhães, P.S
Bresilla, K
Manfrini, L
Boini, A
Perulli, G
Morandi, B
Grappadelli, L.C
Topics
Spatial Variability in Crop, Soil and Natural Resources
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change)
Engineering Technologies
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2008
2018
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Filter results4 paper(s) found.

1. On-The-Go pH Sensor: An Evaluation in a Kentucky Field

A commercially available on-the-go soil pH sensor measures and maps subsurface soil pH at high spatial intensities across managed landscapes.  The overall purpose of this project was to evaluate the potential for this sensor to be used in agricultural fields. The specific goals were to determine and evaluate 1) the accuracy with which this instrument can be calibrated, 2) the geospatial structure of soil pH measurements,... T. Mueller, E. Gianello, B. Mijatovic, E. Rienzi, M. Rodrigues

2. Climatological Diagnostic Analysis: A Case Study for Parbhani District in Marathwada Region of India

... S.N. Pawar, A.K. Gore, G.U. Shinde, M.S. Pendke

3. Technological Improvement on Sugar Cane Yield Monitor

This paper presents the technological improvement on sugar cane yield monitor. The system designed employs load cells as an instrument for weighing billets, set up on the side conveyor of the harvester before the sugar cane billets are dropped into a field transport wagon. This data, along with the information gathered by GPS installed on the harvester, enabled the elaboration of a digital yield map using GIS. In order to improve the yield monitor a re-design of the first prototype was accomplished.... D.G. Cerri, G.R. Gray, P.S. Magalhães

4. 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