Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves

高光谱成像 氮气 植被(病理学) 作物 环境科学 数学 水田 土壤质地 农学 人工智能 土壤科学 计算机科学 化学 土壤水分 生物 医学 有机化学 病理
作者
Dunliang Wang,Rui Li,Tao Liu,Shengping Liu,Chengming Sun,Wenshan Guo
出处
期刊:Field Crops Research [Elsevier]
卷期号:304: 109175-109175 被引量:10
标识
DOI:10.1016/j.fcr.2023.109175
摘要

Nitrogen is one of the important elements of crops, which plays a decisive role in crop growth and development and the formation of yields. Monitoring of rice organ-scale nitrogen concentration based on the unmanned aerial vehicle (UAV) images is of great significance for rice field management and yield prediction. Previous studies have focused on the use of traditional statistical methods and chlorophyll-related vegetation indices to construct plant nitrogen concentration, with models lacking generalizability. In this study, rice field trials of two varieties (NJ9108, YD6) and nitrogen fertilizer treatments (N0-N3: 0, 105, 210 and 315 kg/ha) were conducted for 3 years with manual sampling and UAV digital and hyperspectral images during key fertility periods. Based on the data of the whole growth periods and combined with vegetation indices (VIs), color indices (CIs), hyperspectral parameters (HPs), texture indices (TIs) and machine-learning algorithms, monitoring models of nitrogen concentration at the organ scale of rice were constructed and used to estimate the N content of multiple organs (leaf and stem) of rice at different periods. Field experiments were used to collect the multi-organ nitrogen concentration of rice and the remote sensing (RS) data of UAV during the critical growth period of the two years (2021, 2022), and machine-learning algorithms were used to construct the estimation models. The results showed that VIs had good correlations with leaf nitrogen concentration (LNC), stem nitrogen concentration (SNC) and plant nitrogen concentration (PNC), with correlation coefficients (r) of 0.86, 0.74 and 0.81, respectively. Machine learning estimation models combining multiple types of RS indices were more accurate than single parameter models constructed by traditional statistical methods, with the LNC optimal model (R2 = 0.8, RMSE = 3.83 mg/g), the SNC optimal model (R2 = 0.7, RMSE = 2.43 mg/g) and the PNC optimal model (R2 = 0.7, RMSE = 3.19 mg/g). Validated using data from 2020, the machine-learning models were far more accurate than traditional methods. These results show that the use of multi-source remote sensing data based on machine-learning algorithms can effectively estimate the nitrogen concentration of organs in rice. This study provides an accurate, stable and universal method for estimating rice nitrogen concentration in rice organs, which can be used as a reference for estimating rice nitrogen concentration in large fields using UAV RS technology.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
鸟鸟传教士完成签到,获得积分10
刚刚
刚刚
cjl关闭了cjl文献求助
刚刚
zly完成签到 ,获得积分10
2秒前
包容的涔雨完成签到,获得积分10
2秒前
小羽发布了新的文献求助10
2秒前
大尾巴发布了新的文献求助10
3秒前
燕子发布了新的文献求助10
3秒前
桐桐应助blue2021采纳,获得10
3秒前
耍酷花卷发布了新的文献求助10
4秒前
4秒前
丘比特应助让我康康采纳,获得10
7秒前
CipherSage应助hu采纳,获得10
8秒前
BreadCheems发布了新的文献求助10
9秒前
茸茸茸发布了新的文献求助10
9秒前
慕青应助小羽采纳,获得10
10秒前
11秒前
13秒前
寒冷荠完成签到,获得积分10
14秒前
14秒前
碧蓝世界完成签到 ,获得积分10
14秒前
Singularity应助神内小大夫采纳,获得10
15秒前
16秒前
茸茸茸完成签到,获得积分10
16秒前
汉堡包应助飞云采纳,获得10
18秒前
blue2021发布了新的文献求助10
19秒前
20秒前
21秒前
22秒前
22秒前
hu发布了新的文献求助10
22秒前
22秒前
guojingjing发布了新的文献求助10
23秒前
24秒前
耍酷花卷完成签到,获得积分10
24秒前
Serein发布了新的文献求助10
26秒前
敏酱12138完成签到,获得积分10
26秒前
27秒前
WY完成签到 ,获得积分10
27秒前
lalala发布了新的文献求助10
29秒前
高分求助中
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Smart but Scattered: The Revolutionary Executive Skills Approach to Helping Kids Reach Their Potential (第二版) 1000
Very-high-order BVD Schemes Using β-variable THINC Method 830
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 800
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3248438
求助须知:如何正确求助?哪些是违规求助? 2891833
关于积分的说明 8268874
捐赠科研通 2559834
什么是DOI,文献DOI怎么找? 1388717
科研通“疑难数据库(出版商)”最低求助积分说明 650798
邀请新用户注册赠送积分活动 627775