|Title||A review on drone-based data solutions for cereal crops|
|Author(s)||Panday, Uma Shankar; Pratihast, Arun Kumar; Aryal, Jagannath; Kayastha, Rijan Bhakta|
|Source||Drones 4 (2020)3. - ISSN 2504-446X - 29 p.|
Earth Observation and Environmental Informatics
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Cereals - Citizen science - COVID-19 - Drones - Food security - IoT - Low-cost sensors - Machine learning methods - Precision agriculture - Scaling up|
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.