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Moulay, H
Ram, E
Fajardo, M
Veiga, J.P
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Authors
Veiga, J.P
Cavalcante, D.S
Molin, J.P
Ram, E
Shechter, M
Sela, E
Filippi, P
Jones, E.J
Fajardo, M
Whelan, B.M
Bishop, T.F
Whelan, B
Fajardo, M
Moulay, H
Arnall, B
Phillips, S
Topics
Proximal Sensing in Precision Agriculture
Big Data, Data Mining and Deep Learning
Decision Support Systems
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Type
Oral
Poster
Year
2014
2008
2018
2024
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Filter results5 paper(s) found.

1. Measuring And Mapping Sugarcane Gaps

Sugarcane is an important crop in tropical regions of the world and especially for Brazil, the largest sugar supplier in the market, also running a domestic fleet of flex-fuel driven vehicles based on ethanol. Site specific production management can impact sugarcane production by increasing yield and reducing cost. Sugarcane fields are planted each five years, in average, and an important parameter that is measured after the planting operation is the gaps caused by problems during planting... J.P. Veiga, D.S. Cavalcante, J.P. Molin

2. High Capacity System for Precision Agriculture Reconnaissance and Intelligence

Icaros-Demeter has developed a lightweight, compact remote sensing system with a potential for producing 100,000 acre (400km-2) thematic maps per day with high resolution digital RGB/CIR CMOS sensors. The Icaros- Demeter system enables fast, precise location of multiple area and spots types. The system’s ability for producing high precision Digital Surface Models (DSM) over vast areas, offers a direct method for computing agricultural biomass via volume calculations, instead of common indirect... E. Ram, M. Shechter, E. Sela

3. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine Learning

The ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop

4. Optimising Nitrogen Use in Cereal Crops Using Site-Specific Management Classes and Crop Reflectance Sensors

The relative cost of Nitrogen (N) fertilisers in a cropping input budget, the 33% Nitrogen use efficiency (NUE) seen in global cereal grain production and the potential environmental costs of over-application are leading to changes in the application rates and timing of N fertiliser. Precision agriculture (PA) provides tools for producers to achieve greater synchrony between N supply and crop N demand. To help achieve these goals this research has explored the use of management classes derived... B. Whelan, M. Fajardo

5. Comparative Analysis of Different On-the-go Soil Sensor Systems

This study is part of the field of precision agriculture. This management mode is one of the great revolutions in the agriculture field, and it means better management of farm inputs such as fertilizers, herbicides, and seeds by applying the right amount at the right place and at the right time. To succeed in this, we should dispose of a tool that allows a precise assessment of the soil’s physical state. Thus, on-the-go soil sensors can be used as a creative tool to gain better... H. Moulay, B. Arnall, S. Phillips