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Saxena, A
Schueller, J.K
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Authors
Zhou, C
Lee, W
Pourreza, A
Schueller, J.K
Liburd, O.E
Ampatzidis, Y
Zuniga-Ramirez, G
Saxena, A
Dash, M
Verma, A.P
Topics
Big Data, Data Mining and Deep Learning
Geospatial Data
Type
Oral
Poster
Year
2022
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1. 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

2. Cloud Correction of Sentinel-2 NDVI Using S2cloudless Package

Optical satellite-derived Normalized Difference Vegetation Index (NDVI) is by far the most commonly used vegetation index value for crop monitoring. However, it is quite sensitive to the cloud, and cloud shadows and significantly decreases its usability, especially in agricultural applications. Therefore, an accurate and reliable cloud correction method is mandatory for its effective application. To address this issue, we have developed an approach to correct the NDVI values of each and every... A. Saxena, M. Dash, A.P. Verma