Proceedings :: International Society of Precision Agriculture

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Gross, B

Sherafat, A

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Authors

Levitan, N

Gross, B

Bhandari, S

Acosta, M

Cordova Gonzalez, C

Raheja, A

Sherafat, A

Topics

Proximal and Remote Sensing of Soil and Crop (including Phenotyping)

Artificial Intelligence (AI) in Agriculture

Type

Oral

Year

2018

2024

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Filter results2 paper(s) found.
1. Assessment of the Information Content in Solar Reflective Satellite Measurements with Respect to Crop Growth Model State Variables To increase the utilization of satellite remote sensing data in precision agriculture, it is necessary to retrieve the most relevant variables from the satellite signals so that the retrievals can be directly utilized by agricultural management entities. The variables that make up the state vector description of existing crop growth models provide inherent relevance to on-farm decision making because they can be used to predict future crop status based on changing farm inputs. In this study, the... N. Levitan, B. Gross Download paper
2. Leveraging UAV-based Hyperspectral Data and Machine Learning Techniques for the Detection of Powderly Mildew in Vineyards This paper presents the development and validation of machine learning models for the detection of powdery mildew in vineyards. The models are trained and validated using custom datasets obtained from unmanned aerial vehicles (UAVs) equipped with a hyperspectral sensor that can collect images in visible/near-infrared (VNIR) and shortwave infrared (SWIR) wavelengths. The dataset consists of the images of vineyards with marked regions for powdery mildew, meticulously annotated using LabelImg. ... S. Bhandari, M. Acosta, C. Cordova gonzalez, A. Raheja, A. Sherafat Download paper

Filter results2 paper(s) found.

1. Assessment of the Information Content in Solar Reflective Satellite Measurements with Respect to Crop Growth Model State Variables

To increase the utilization of satellite remote sensing data in precision agriculture, it is necessary to retrieve the most relevant variables from the satellite signals so that the retrievals can be directly utilized by agricultural management entities. The variables that make up the state vector description of existing crop growth models provide inherent relevance to on-farm decision making because they can be used to predict future crop status based on changing farm inputs. In this study, the... N. Levitan, B. Gross

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2. Leveraging UAV-based Hyperspectral Data and Machine Learning Techniques for the Detection of Powderly Mildew in Vineyards

This paper presents the development and validation of machine learning models for the detection of powdery mildew in vineyards. The models are trained and validated using custom datasets obtained from unmanned aerial vehicles (UAVs) equipped with a hyperspectral sensor that can collect images in visible/near-infrared (VNIR) and shortwave infrared (SWIR) wavelengths. The dataset consists of the images of vineyards with marked regions for powdery mildew, meticulously annotated using LabelImg. ... S. Bhandari, M. Acosta, C. Cordova gonzalez, A. Raheja, A. Sherafat

Download paper