Drone and handheld sensors for hemp: Evaluating NDVI and NDRE in relation to nitrogen application and crop yield

Abstract Understanding the effect of nitrogen (N) rates on hemp ( Cannabis sativa ) cultivation is crucial for optimizing crop yield and quality. This study evaluated the effectiveness of handheld (active) and drone (passive) sensors in measuring crop reflectance and predicting key growth parameters in response to varying N application rates. The study was conducted during the summer of 2022 at the Plant Science Research and Education Unit in Citra, FL. The trial involved three hemp cultivars—NWG‐2730, Yuma, and IH‐Williams—subjected to six N rates (0, 56, 112, 168, 224, and 280 kg/ha). Reflectance data were collected at 76 days after planting to calculate normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE). Results indicated that increased N rates led to higher NDVI and NDRE values in cultivars that had not yet reached senescence. NDVI from the drone sensor showed the strongest relationship with N rates and was the most accurate predictor for in‐season biomass yield, final biomass yield, and plant height. However, the predictive efficiency of NDVI and NDRE varied by cultivar and decreased as plants approached senescence. Early‐season crop reflectance sensing proved more reliable due to the lower impact of senescent leaves. The study highlights the potential of sensor technology in hemp cultivation, offering insights into yield forecasting, variable N management, and high‐throughput phenotyping. Future research should further explore the application of sensors to enhance precision agriculture practices in hemp cultivation.

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Publication Details

Journal
Agrosystems Geosciences & Environment
Published
2025-03-01
DOI
https://doi.org/10.1002/agg2.70075
Citations
7
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
12.01

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article

Drone and handheld sensors for hemp: Evaluating NDVI and NDRE in relation to nitrogen application and crop yield

Hardev S. Sandhu, Zachary Brym, Hardeep Singh, Tamara Serrano et al.
7 citations
Agrosystems Geosciences & Environment
Remote Sensing in Agriculture
12.01
article

Drone and handheld sensors for hemp: Evaluating NDVI and NDRE in relation to nitrogen application and crop yield

Hardev S. Sandhu, Zachary Brym, Hardeep Singh, Tamara Serrano, Lakesh K. Sharma, ADITYA KUMAR SINGH, Winniefred Griffin, Ayush K. Sharma, Navdeep Kaur, Hayden Shellenbarger
article en
7 citations

Abstract

Abstract Understanding the effect of nitrogen (N) rates on hemp ( Cannabis sativa ) cultivation is crucial for optimizing crop yield and quality. This study evaluated the effectiveness of handheld (active) and drone (passive) sensors in measuring crop reflectance and predicting key growth parameters in response to varying N application rates. The study was conducted during the summer of 2022 at the Plant Science Research and Education Unit in Citra, FL. The trial involved three hemp cultivars—NWG‐2730, Yuma, and IH‐Williams—subjected to six N rates (0, 56, 112, 168, 224, and 280 kg/ha). Reflectance data were collected at 76 days after planting to calculate normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE). Results indicated that increased N rates led to higher NDVI and NDRE values in cultivars that had not yet reached senescence. NDVI from the drone sensor showed the strongest relationship with N rates and was the most accurate predictor for in‐season biomass yield, final biomass yield, and plant height. However, the predictive efficiency of NDVI and NDRE varied by cultivar and decreased as plants approached senescence. Early‐season crop reflectance sensing proved more reliable due to the lower impact of senescent leaves. The study highlights the potential of sensor technology in hemp cultivation, offering insights into yield forecasting, variable N management, and high‐throughput phenotyping. Future research should further explore the application of sensors to enhance precision agriculture practices in hemp cultivation.

Agrosystems Geosciences & EnvironmentVol. 8(1)
Pennsylvania State University (US), Everglades University (US), University of Florida (US), Southwest Florida Research (US)
Florida Department of Agriculture and Consumer Services
Openalex Percentile: Top 2%
Remote Sensing in Agriculture
12.01
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