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UAV Phenotyping

Globally, agriculture is witnessing significant technological advancement, best exemplified by the Agriculture 4.0 movement. India, one of the world’s largest food producers and consumers, has to stay abreast of the growth.

Remote-sensing imaging for monitoring crop health and security is one of the technologies yet to come of age in India.

Evolving regulations and lack of expertise are teething troubles set to pave way for Unmanned Aerial Vehicles (UAVs) with Red Green Blue (RGB) and multispectral/hyperspectral cameras for accelerated data collection. Imaging with UAV is being done elsewhere in the world to support crop improvement and precision agriculture. Drones are also being used for accurate and targeted pesticide spraying, thus minimizing human exposure.

To give drones a leg up in India, the Indian Institute of Technology-Hyderabad and ICRISAT are collaborating to standardize phenotyping protocols using UAV-based technologies to support crop improvement programs.

 

Why standardization

Establishing ground rules before flying drones over fields can help efficient image capture and analysis. Make of the drone, location, wind speed, flight height, image capture frequency, and solar radiation are critical parameters that determine outcome i.e. the quality of data that images can provide. While making fixed prescriptions for each of these parameters would be counterproductive, understanding a range of operating conditions within which optimum image capture results can greatly augment phenotyping efforts.

More importantly, standard operating procedures can be reliably replicated across institutions, geographies or crops. The ICIRSAT-IIT team, supported by Bill & Melinda Gates Foundation (BMGF), Indian government’s Ministry of Electronics and Information Technology, CGIAR’s Excellence in Breeding Platform (EiB) and Grain Legumes and Dryland Cereals (GLDC), and International Food Policy Research Institute (IFPRI), aim to develop SOPs for image capture and analysis.

Dr Vincent Vadez, Principal Scientist, IRD, France, who is the Project Collaborator (ICRISAT) and Module 4 Leader of (EiB), said, “EiB is currently developing an initiative addressing the main bottlenecks. These are- (i) the need for SOP to generate quality images and (ii) access to automated, high quality and high performance data analysis pipeline to generate trustworthy crop indices with a fast turnover time to support breeding selections. These two limitations were recognized after a workshop with some of the most advanced global groups in the domain of drone technology development that support breeding,”.

Get set, go!

 

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