Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 569266
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.
DOI https://doi.org/10.3390/drones4030041
Department(s) Earth Observation and Environmental Informatics
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2020
Keyword(s) Cereals - Citizen science - COVID-19 - Drones - Food security - IoT - Low-cost sensors - Machine learning methods - Precision agriculture - Scaling up
Abstract

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.

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