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
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....
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | ISPRS Open Journal of Photogrammetry and Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667393223000133 |
| _version_ | 1797660483665264640 |
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| author | Gattu Priyanka Sunita Choudhary Krithika Anbazhagan Dharavath Naresh Rekha Baddam Jan Jarolimek Yogesh Parnandi P. Rajalakshmi Jana Kholova |
| author_facet | Gattu Priyanka Sunita Choudhary Krithika Anbazhagan Dharavath Naresh Rekha Baddam Jan Jarolimek Yogesh Parnandi P. Rajalakshmi Jana Kholova |
| author_sort | Gattu Priyanka |
| collection | DOAJ |
| description | 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 (∼300 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. |
| first_indexed | 2024-03-11T18:30:32Z |
| format | Article |
| id | doaj.art-ffa34adf36754cc0b141899421ee99ce |
| institution | Directory Open Access Journal |
| issn | 2667-3932 |
| language | English |
| last_indexed | 2024-03-11T18:30:32Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ISPRS Open Journal of Photogrammetry and Remote Sensing |
| spelling | doaj.art-ffa34adf36754cc0b141899421ee99ce2023-10-13T11:06:13ZengElsevierISPRS Open Journal of Photogrammetry and Remote Sensing2667-39322023-08-019100042A 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 imagesGattu Priyanka0Sunita Choudhary1Krithika Anbazhagan2Dharavath Naresh3Rekha Baddam4Jan Jarolimek5Yogesh Parnandi6P. Rajalakshmi7Jana Kholova8Department of Electrical Engineering, Indian Institute of Technology Hyderabad (IITH), Hyderabad, 502285, Telangana, India; Corresponding author.Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, IndiaCrops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Hyderabad (IITH), Hyderabad, 502285, Telangana, IndiaCrops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, IndiaDepartment of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamýcká 129, Prague, 16500, Czech RepublicCrops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Hyderabad (IITH), Hyderabad, 502285, Telangana, IndiaCrops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India; Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamýcká 129, Prague, 16500, Czech RepublicThe 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 (∼300 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.http://www.sciencedirect.com/science/article/pii/S2667393223000133Accuracy of plant trait predictionCrop phenotypingImage qualityUAV-Based sensing |
| spellingShingle | Gattu Priyanka Sunita Choudhary Krithika Anbazhagan Dharavath Naresh Rekha Baddam Jan Jarolimek Yogesh Parnandi P. Rajalakshmi Jana Kholova 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 Accuracy of plant trait prediction Crop phenotyping Image quality UAV-Based sensing |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Accuracy of plant trait prediction Crop phenotyping Image quality UAV-Based sensing |
| url | http://www.sciencedirect.com/science/article/pii/S2667393223000133 |
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