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Open AccessFeature PaperReview

A Review on Drone-Based Data Solutions for Cereal Crops

1
Department of Geomatics Engineering, School of Engineering, Kathmandu University, Dhulikhel 45200, Nepal
2
Wageningen Environmental Research, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
3
Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
4
Department of Environmental Science and Engineering, School of Science, Kathmandu University, Dhulikhel 45200, Nepal
*
Author to whom correspondence should be addressed.
Drones 2020, 4(3), 41; https://doi.org/10.3390/drones4030041
Received: 7 July 2020 / Revised: 9 August 2020 / Accepted: 10 August 2020 / Published: 12 August 2020
Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella. View Full-Text
Keywords: precision agriculture; cereals; drones; machine learning methods; scaling up; citizen science; low-cost sensors; IoT; COVID-19; food security precision agriculture; cereals; drones; machine learning methods; scaling up; citizen science; low-cost sensors; IoT; COVID-19; food security
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MDPI and ACS Style

Panday, U.S.; Pratihast, A.K.; Aryal, J.; Kayastha, R.B. A Review on Drone-Based Data Solutions for Cereal Crops. Drones 2020, 4, 41. https://doi.org/10.3390/drones4030041

AMA Style

Panday US, Pratihast AK, Aryal J, Kayastha RB. A Review on Drone-Based Data Solutions for Cereal Crops. Drones. 2020; 4(3):41. https://doi.org/10.3390/drones4030041

Chicago/Turabian Style

Panday, Uma S.; Pratihast, Arun K.; Aryal, Jagannath; Kayastha, Rijan B. 2020. "A Review on Drone-Based Data Solutions for Cereal Crops" Drones 4, no. 3: 41. https://doi.org/10.3390/drones4030041

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