Determining Variable Rate Fertilizer Dosage in Forage Maize Farm Using Multispectral UAV Imagery

IF 2.2 4区 地球科学 Q3 ENVIRONMENTAL SCIENCES Journal of the Indian Society of Remote Sensing Pub Date : 2024-09-03 DOI:10.1007/s12524-024-01976-1
Nikrooz Bagheri, Maryam Rahimi Jahangirlou, Mehryar Jaberi Aghdam
{"title":"Determining Variable Rate Fertilizer Dosage in Forage Maize Farm Using Multispectral UAV Imagery","authors":"Nikrooz Bagheri, Maryam Rahimi Jahangirlou, Mehryar Jaberi Aghdam","doi":"10.1007/s12524-024-01976-1","DOIUrl":null,"url":null,"abstract":"<p>This research aimed to evaluate the capability of the combination of aerial UAV multispectral imagery and an equation-oriented approach for monitoring nitrogen status and variable-rate nitrogen fertilizer management in forage maize farms. To achieve this goal, four levels of nitrogen fertilizer were applied in a randomized complete block design (0, 50, 100, and 150%) in eight-leaf and tasseling growth stages. A method based on the biomass of aerial organs and leaf nitrogen content was used to estimate variable rate nitrogen application. Among vegetative indices extracted from aerial images, the correlation between the Normalized Difference Vegetation Index (r = 0.77, <i>P</i> ≤ 0.01), Nitrogen Reflectance Index (r = 0.70, <i>P</i> ≤ 0.01) and Modified Triangular Vegetation Index2 (r = 0.67, <i>P</i> ≤ 0.01) with leaf nitrogen content were positive and significant at the eight-leaf growth stage. Similarly, the Normalized Difference Vegetation Index (r = 0.77, <i>P</i> ≤ 0.01), Nitrogen Reflectance Index (r = 0.87, <i>P</i> ≤ 0.01) and Modified Triangular Vegetation Index2 (r = 0.66, <i>P</i> ≤ 0.01) had a high correlation with the leaf nitrogen content at the tasseling growth stage. Based on the obtained results, a total of 223, 192, 173, and 100 kg/ha urea fertilizer were estimated to be applied in 0, 50, 100, and 150% nitrogen fertilizer plots, respectively. Findings suggested that the nitrogen changes and nitrogen rate needed to apply were detected by aerial multispectral imagery with good accuracy.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"19 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Indian Society of Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12524-024-01976-1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

This research aimed to evaluate the capability of the combination of aerial UAV multispectral imagery and an equation-oriented approach for monitoring nitrogen status and variable-rate nitrogen fertilizer management in forage maize farms. To achieve this goal, four levels of nitrogen fertilizer were applied in a randomized complete block design (0, 50, 100, and 150%) in eight-leaf and tasseling growth stages. A method based on the biomass of aerial organs and leaf nitrogen content was used to estimate variable rate nitrogen application. Among vegetative indices extracted from aerial images, the correlation between the Normalized Difference Vegetation Index (r = 0.77, P ≤ 0.01), Nitrogen Reflectance Index (r = 0.70, P ≤ 0.01) and Modified Triangular Vegetation Index2 (r = 0.67, P ≤ 0.01) with leaf nitrogen content were positive and significant at the eight-leaf growth stage. Similarly, the Normalized Difference Vegetation Index (r = 0.77, P ≤ 0.01), Nitrogen Reflectance Index (r = 0.87, P ≤ 0.01) and Modified Triangular Vegetation Index2 (r = 0.66, P ≤ 0.01) had a high correlation with the leaf nitrogen content at the tasseling growth stage. Based on the obtained results, a total of 223, 192, 173, and 100 kg/ha urea fertilizer were estimated to be applied in 0, 50, 100, and 150% nitrogen fertilizer plots, respectively. Findings suggested that the nitrogen changes and nitrogen rate needed to apply were detected by aerial multispectral imagery with good accuracy.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用多光谱无人机图像确定饲用玉米农场的变速肥料用量
本研究旨在评估航空无人机多光谱成像与方程导向法相结合的能力,以监测饲用玉米农场的氮肥状况和变速氮肥管理。为实现这一目标,采用随机完全区组设计,在八叶期和抽穗期施用四种氮肥水平(0、50、100 和 150%)。采用基于气生器官生物量和叶片含氮量的方法来估算不同的氮肥施用量。从航空图像中提取的植被指数中,归一化差异植被指数(r = 0.77,P ≤ 0.01)、氮反射率指数(r = 0.70,P ≤ 0.01)和修正三角植被指数2(r = 0.67,P ≤ 0.01)与八叶生长阶段的叶片含氮量呈显著正相关。同样,在抽穗生长阶段,归一化差异植被指数(r = 0.77,P ≤ 0.01)、氮反射指数(r = 0.87,P ≤ 0.01)和修正三角形植被指数2(r = 0.66,P ≤ 0.01)与叶片含氮量具有高度相关性。根据所得结果,估计氮肥施用量为 0%、50%、100% 和 150%的地块分别需要施用 223、192、173 和 100 公斤/公顷的尿素肥料。研究结果表明,航空多光谱图像能准确检测出氮肥的变化和所需的施氮量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of the Indian Society of Remote Sensing
Journal of the Indian Society of Remote Sensing ENVIRONMENTAL SCIENCES-REMOTE SENSING
CiteScore
4.80
自引率
8.00%
发文量
163
审稿时长
7 months
期刊介绍: The aims and scope of the Journal of the Indian Society of Remote Sensing are to help towards advancement, dissemination and application of the knowledge of Remote Sensing technology, which is deemed to include photo interpretation, photogrammetry, aerial photography, image processing, and other related technologies in the field of survey, planning and management of natural resources and other areas of application where the technology is considered to be appropriate, to promote interaction among all persons, bodies, institutions (private and/or state-owned) and industries interested in achieving advancement, dissemination and application of the technology, to encourage and undertake research in remote sensing and related technologies and to undertake and execute all acts which shall promote all or any of the aims and objectives of the Indian Society of Remote Sensing.
期刊最新文献
A Heuristic Approach of Radiometric Calibration for Ocean Colour Sensors: A Case Study Using ISRO’s Ocean Colour Monitor-2 Farmland Extraction from UAV Remote Sensing Images Based on Improved SegFormer Model Self Organizing Map based Land Cover Clustering for Decision-Level Jaccard Index and Block Activity based Pan-Sharpened Images Improved Building Extraction from Remotely Sensed Images by Integration of Encode–Decoder and Edge Enhancement Models Enhancing Change Detection Accuracy in Remote Sensing Images Through Feature Optimization and Game Theory Classifier
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1