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Biswas, A

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Authors

Biswas, A

Fu, Y

Taneja, P

Lin, S

Daggupati, P

Vasava, H

Gobezie, T.B

Biswas, A

Topics

Proximal and Remote Sensing

Policy Support Innovations

Type

Oral

Year

2020

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Filter results2 paper(s) found.
1. Estimating soil organic carbon from cell phone images Soil organic matter (SOM) is considered as the backbone of soil health and soil quality. Thus, its’ estimation is critical to support the development of management decision including precision agriculture. To overcome challenges of laborious, rather expensive and time-consuming laboratory measurements, recent advances in image acquisition systems provided a new dimension of image-based SOM prediction. However, challenges remain in using soil images taken directly in the field due to variable... A. Biswas, Y. Fu, P. Taneja, S. Lin, P. Daggupati, H. Vasava Download paper
2. Just a moment; the need for streamlining precision agriculture data in Africa Precision agriculture (PA) data sources in the era of digital agriculture are diverse in terms of the range of technology options and the types of data they generate. These include proximal sensors, unmanned aerial vehicle (UAV), satellites, farm machinery mounted sensors and robotics to generate static data or real time information (e.g., yield monitoring). Government institutions, scientists and private sectors take the lion’s share in generating PA data at innovation, validation and dissemination... T.B. Gobezie, A. Biswas Download paper

Filter results2 paper(s) found.

1. Estimating soil organic carbon from cell phone images

Soil organic matter (SOM) is considered as the backbone of soil health and soil quality. Thus, its’ estimation is critical to support the development of management decision including precision agriculture. To overcome challenges of laborious, rather expensive and time-consuming laboratory measurements, recent advances in image acquisition systems provided a new dimension of image-based SOM prediction. However, challenges remain in using soil images taken directly in the field due to variable... A. Biswas, Y. Fu, P. Taneja, S. Lin, P. Daggupati, H. Vasava

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2. Just a moment; the need for streamlining precision agriculture data in Africa

Precision agriculture (PA) data sources in the era of digital agriculture are diverse in terms of the range of technology options and the types of data they generate. These include proximal sensors, unmanned aerial vehicle (UAV), satellites, farm machinery mounted sensors and robotics to generate static data or real time information (e.g., yield monitoring). Government institutions, scientists and private sectors take the lion’s share in generating PA data at innovation, validation and dissemination... T.B. Gobezie, A. Biswas

Download paper