Proceedings :: International Society of Precision Agriculture

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Sprintsin, M

Schueller, J.K

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Authors

Cohen, Y

Alchanatis, V

Heuer, B

Lemcoff, H

Sprintsin, M

Rosen, C

Mulla, D

Nigon, T

Dar, Z

Cohen, A

Levi, A

Brikman, R

Markovits, T

Rud, R

Zhou, C

Lee, W

Pourreza, A

Schueller, J.K

Liburd, O.E

Ampatzidis, Y

Zuniga-Ramirez, G

Topics

Remote Sensing Applications in Precision Agriculture

Big Data, Data Mining and Deep Learning

Type

Poster

Oral

Year

2012

2022

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Filter results2 paper(s) found.
1. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial Images Potato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud Download paper
2. Strawberry Pest Detection Using Deep Learning and Automatic Imaging System Strawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality. However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cameras... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez Download paper

Filter results2 paper(s) found.

1. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial Images

Potato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud

Download paper

2. Strawberry Pest Detection Using Deep Learning and Automatic Imaging System

Strawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality. However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cameras... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez

Download paper