Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height

Rahul Raj (), Jeffrey P. Walker and Adinarayana Jagarlapudi
Additional contact information
Rahul Raj: Centre of Studies in Resources Engineering, IITB-Monash Research Academy, IIT Bombay, Powai, Mumbai 400076, India
Jeffrey P. Walker: Department of Civil Engineering, Monash University, Clayton, Melbourne 3800, Australia
Adinarayana Jagarlapudi: Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

Agriculture, 2023, vol. 13, issue 7, 1-14

Abstract: The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach.

Keywords: crop healthiness; drone sensing; precision agriculture; APSIM (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/7/1292/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/7/1292/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:7:p:1292-:d:1178270

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().