{"title":"Drone and handheld sensors for hemp: Evaluating NDVI and NDRE in relation to nitrogen application and crop yield","authors":"Navdeep Kaur, Ayush K. Sharma, Hayden Shellenbarger, Winniefred Griffin, Tamara Serrano, Zachary Brym, Aditya Singh, Hardeep Singh, Hardev Sandhu, Lakesh K. Sharma","doi":"10.1002/agg2.70075","DOIUrl":null,"url":null,"abstract":"<p>Understanding the effect of nitrogen (N) rates on hemp (<i>Cannabis sativa</i>) 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.</p>","PeriodicalId":7567,"journal":{"name":"Agrosystems, Geosciences & Environment","volume":"8 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agg2.70075","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agrosystems, Geosciences & Environment","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/agg2.70075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
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.