A - Papers appearing in refereed journals
Fraccaro, P., Butt, J., Edwards, B., Freckleton, R. P., Childs, D. Z., Reusch, K. and Comont, D. 2022. A Deep Learning Application to Map Weed Spatial Extent from Unmanned Aerial Vehicles Imagery. Remote Sensing. 14 (17), p. 4197. https://doi.org/10.3390/rs14174197
Authors | Fraccaro, P., Butt, J., Edwards, B., Freckleton, R. P., Childs, D. Z., Reusch, K. and Comont, D. |
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Abstract | Weed infestation is a global threat to agricultural productivity, leading to low yields and financial losses. Weed detection, based on applying machine learning to imagery collected by Unmanned Aerial Vehicles (UAV) has shown potential in the past; however, validation on large data-sets (e.g., across a wide number of different fields) remains lacking, with few solutions actually made operational. Here, we demonstrate the feasibility of automatically detecting weeds in winter wheat fields based on deep learning methods applied to UAV data at scale. Focusing on black-grass (the most pernicious weed across northwest Europe), we show high performance (i.e., accuracy above 0.9) and highly statistically significant correlation (i.e., ro > 0.75 and p < 0.00001) between imagery-derived local and global weed maps and out-of-bag field survey data, collected by experts over 31 fields (205 hectares) in the UK. We demonstrate how the developed deep learning model can be made available via an easy-to-use docker container, with results accessible through an interactive dashboard. Using this approach, clickable weed maps can be created and deployed rapidly, allowing the user to explore actual model predictions for each field. This shows the potential for this approach to be used operationally and influence agronomic decision-making in the real world. |
Keywords | Deep learning; Image Segmentation; Unmanned Aerial Vehicle; Weed Detection; Black-grass |
Year of Publication | 2022 |
Journal | Remote Sensing |
Journal citation | 14 (17), p. 4197 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/rs14174197 |
Web address (URL) | https://www.mdpi.com/2072-4292/14/17/4197/htm |
Open access | Published as ‘gold’ (paid) open access |
Funder | Innovate UK |
Funder project or code | aiScope - AI data platform for smart crop protection |
Publisher's version | |
Output status | Published |
Publication dates | |
Online | 26 Aug 2022 |
Publication process dates | |
Accepted | 17 Aug 2022 |
Submitted | 18 Jul 2022 |
Publisher | MDPI |
ISSN | 2072-4292 |
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