A step towards inter-operable Unmanned Aerial Vehicles (UAV) based phenotyping; A case study demonstrating a rapid, quantitative approach to standardize image acquisition and check quality of acquired images

Priyanka, G and Choudhary, S and Anbazhagan, K and Naresh, D and Baddam, R and Jarolímek, J and Parnandi, Y and Rajalakshmi, P and Kholova, J (2023) A step towards inter-operable Unmanned Aerial Vehicles (UAV) based phenotyping; A case study demonstrating a rapid, quantitative approach to standardize image acquisition and check quality of acquired images. ISPRS Open Journal of Photogrammetry and Remote Sensing, 9. pp. 1-13. ISSN 2667-3932

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Abstract

The Unmanned aerial vehicles (UAVs) - based imaging is being intensively explored for precise crop evaluation. Various optical sensors, such as RGB, multi-spectral, and hyper-spectral cameras, can be used for this purpose. Consistent image quality is crucial for accurate plant trait prediction (i.e., phenotyping). However, achieving consistent image quality can pose a challenge as image qualities can be affected by i) UAV and camera technical settings, ii) environment, and iii) crop and field characters which are not always under the direct control of the UAV operator. Therefore, capturing the images requires the establishment of robust protocols to acquire images of suitable quality, and there is a lack of systematic studies on this topic in the public domain. Therefore, in this case study, we present an approach (protocols, tools, and analytics) that addressed this particular gap in our specific context. In our case, we had the drone (DJI Inspire 1 Raw) available, equipped with RGB camera (DJI Zenmuse x5), which needed to be standardized for phenotyping of the annual crops’ canopy cover (CC). To achieve this, we have taken 69 flights in Hyderabad, India, on 5 different cereal and legume crops ( genotypes) in different vegetative growth stages with different combinations of technical setups of UAV and camera and across the environmental conditions typical for that region. For each crop-genotype combination, the ground truth (for CC) was rapidly estimated using an automated phenomic platform (LeasyScan phenomics platform, ICRISAT). This data-set enabled us to 1) quantify the sensitivity of image acquisition to the main technical, environmental and crop-related factors and this analysis was then used to develop the image acquisition protocols specific to our UAV-camera system. This process was significantly eased by automated ground-truth collection. We also 2) identified the important image quality indicators that integrated the effects of 1) and these indicators were used to develop the quality control protocols for inspecting the images post accquisition. To ease 2), we present a web-based application available at (https://github.com/GattuPriyanka/Framework-for-UAV-image-quality.git) which automatically calculates these key image quality indicators. Overall, we present a methodology for establishing the image acquisition protocol and quality check for obtained images, enabling a high accuracy of plant trait inference. This methodology was demonstrated on a particular UAV-camera set-up and focused on a specific crop trait (CC) at the ICRISAT research station (Hyderabad, India). We envision that, in the future, a similar image quality control system could facilitate the interoperability of data from various UAV-imaging set-ups.

Item Type:	Article
Divisions:	Global Research Program - Accelerated Crop Improvement
CRP:	UNSPECIFIED
Uncontrolled Keywords:	Accuracy of plant trait prediction, Crop phenotyping, Image quality, UAV-Based sensing
Subjects:	Others > Crop Physiology
Depositing User:	Mr Nagaraju T
Date Deposited:	22 Feb 2024 08:35
Last Modified:	22 Feb 2024 08:35
URI:	http://oar.icrisat.org/id/eprint/12504
Official URL:	https://www.sciencedirect.com/science/article/pii/...
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	The authors acknowledge Mr. Dharavath Naresh from the IITH team for helping in UAV data acquisition. We also thank the ICRISAT team for generating and accessing ground truth information. We are especially thankful to the team of specialists from Hiphen (especially Dr. Alexis Comar) for supporting the revisions of the manuscript and practical insight into the commercial phenotyping industry (https://www.hiphen-plant.com/.
Links:	Google Scholar

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