Correlation between Spring Wheat Physiological Indicators and UAV Digital Image Index in Hetao Irrigation Area
Source abstract
Abstract
To accurately and non-destructively monitor the growth of spring wheat in the Hetao irrigation area, UAV remote sensing was employed during various fertility stages. Digital image indices from diverse fertilization treatments were calculated and compared with physiological indices to identify the most sensitive digital image indices corresponding to these indices. The study underscored the critical importance of the flowering stage in the growth of spring wheat, thus highlighting the necessity of focusing on this stage. This finding reiterated that the flowering stage was pivotal for spring wheat development in the Hetao Irrigation Area. Several digital image indices, such as GLA, R, G, INT, g, GRVI, MGRVI, RGBVI, EXG, and GRRI, exhibited a high frequency of significant correlations with physiological indices during the four primary reproductive stages of wheat. Consequently, these sensitive digital image indices during the flowering stage can more effectively characterize the physiological indices of spring wheat.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and Land Use · Remote Sensing in Agriculture · Environmental and Agricultural Sciences
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Agriculture, Inner Mongolia Agricultural University, Hohhot 010010, China
Inner Mongolia Technology Extension Center of Agriculture and Animal Husbandry, Hohhot 011799, China
Inner Mongolia Academy of Agriculture and Animal Husbandry, Hohhot 010031, China
Funding
Inner Mongolia Natural Science Foundation Program · Inner Mongolia Basic Research Operating Expenses Colleges and Universities Program · Inner Mongolia Autonomous Region Higher Education Institutions Carbon Peak Carbon Neutral Research Special Project
References (26)
Show source-provided references
1. Omia, E., Bae, H., Park, E., Kim, M.S., Baek, I., Kabenge, I., and Cho, B.-K. (2023). Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses and Recent Advances. Remote Sens., 15.10.3390/rs15020354 ↗
2. Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors10.3389/fpls.2022.1092610 ↗
3. Reliability of NDVI derived by high resolution satellite and UAV compared to in-field methods for the evaluation of early crop N status and grain yield in wheat10.1017/S0014479717000278 ↗
4. Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables10.1007/s11119-018-9609-y ↗
5. Quantifying genetic effects of ground cover on soil water evaporation using digital imaging10.1071/FP09277 ↗
6. Image processing techniques to estimate weight and morphological parameters for selected wheat refractions10.1038/s41598-021-00081-4 ↗
7. Correlation of wheat biomass and yield with UAV image characteristic parameters
8. Poley, L.G., and Mcdermid, G.J. (2020). A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems. Remote Sens., 12.10.3390/rs12071052 ↗
9. Extraction of vegetation information from visible unmanned aerial vehicle images
10. Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning10.1007/s11119-021-09864-1 ↗
11. The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images10.1080/01431161.2018.1455244 ↗
12. Advances in plant nutrition diagnosis based on remote sensing and computer application10.1007/s00521-018-3932-0 ↗
13. Diagnosis of nitrogen nutrient and recommended fertilization in summer corn using leaf digital images of cellphone camera
14. Digital camera based image segmentation of rice canopy and diagnosis of nitrogen nutrition
15. Mao, W., Wang, Y., and Wang, Y.R. (2003). Real-Time Detection of Between-Row Weeds Using Machine Vision, ASABE.
16. Niu, Y., Zhang, L., Zhang, H., Han, W., and Peng, X. (2019). Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery. Remote Sens., 11.10.3390/rs11111261 ↗
17. Prediction of Wheat Plant Height and Leaf Area Index Based on UAV Image
18. N status diagnosis of winter wheat by using digital image analysis technology
19. Analysis of Maize Growth and Nitrogen Nutrition Status Based on Digital Camera Image
20. Nitrogen Status Diagnosis of Rice by Using a Digital Camera
21. Umut, H. (2019). Wheat Leaf Area Index (LAI) Inversion by Using “Satellite-UAV-Ground” Multi-Source Remote Sensing Data. [Ph.D. Thesis, Xinjiang University].
22. Impact of the reproductive organs on crop BRDF as observed from a UAV10.1016/j.rse.2021.112433 ↗
23. An improved approach to estimate ratoon rice aboveground biomass by integrating UAV-based spectral, textural and structural features10.1007/s11119-022-09884-5 ↗
24. Zhang, Z., Flores, P., Igathinathane, C., Igathinathane, C., Naik, D., Kiran, R., and Ransom, J.K. (2020). Wheat lodging detection from UAS imagery using machine learning algorithms. Remote Sens., 12.10.3390/rs12111838 ↗
25. Shi, X.-Y. (2005). Evaluation of Some Physiological Parameters of Winter Wheat Leaves Using Digital Image Processing. [Ph.D. Thesis, Heibei Agriculture University].
26. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light10.1186/1746-4811-10-36 ↗