Detection of Ecballium elaterium in hedgerow olive orchards using a low‐cost uncrewed aerial vehicle and open‐source algorithms

• Published in: Pest management science Vol. 79; no. 2; pp. 645 - 654
• Main Authors: Torres‐Sánchez, Jorge, Mesas‐Carrascosa, Francisco Javier, Pérez‐Porras, Fernando, López‐Granados, Francisca
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.02.2023; Wiley Subscription Services, Inc
• Subjects: Agricultural practices; Algorithms; Cover crops; Crops; Crops, Agricultural; Cucurbitaceae; digital transformation; Ecballium elaterium; Farms; Hedgerows; Herbicides; Image acquisition; machine learning; Olea; Open source software; Orchards; Pest control; Plant Weeds; random forest; remote sensing; site‐specific weed management; squirting cucumber; Tillage; Unmanned aerial vehicles; Vernacular names; Weed control; Weed Control - methods; Weeds; site-specific weed management; remote sensing; random forest; squirting cucumber; machine learning; digital transformation
• ISSN: 1526-498X; 1526-4998
• DOI: 10.1002/ps.7233

BACKGROUND Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter‐row area infesting the natural or sown cover crops, and is considered a hard‐to‐control weed. Research in other woody crops has shown E. elaterium has a patchy distribution, which makes this weed susceptible to design a site‐specific control strategy only addressed to E. elaterium patches. Therefore, the aim of this work was to develop a methodology based on the analysis of imagery acquired with an uncrewed aerial vehicle (UAV) to detect and map E. elaterium infestations in hedgerow olive orchards. RESULTS The study was conducted in two superintensive olive orchards, and the images were taken using a UAV equipped with an RGB sensor. Flights were conducted on two dates: in May, when there were various weeds infesting the orchard, and in September, when E. elaterium was the only infesting weed. UAV‐orthomosaics in the first scenario were classified using random forest models, and the orthomosaics from September with E. elaterium as the only weed, were analyzed using an unsupervised algorithm. In both cases, the overall accuracies were over 0.85, and the producer's accuracies for E. elaterium ranged between 0.74 and 1.00. CONCLUSION These results allow the design of a site‐specific and efficient herbicide control protocol which would represent a step forward in sustainable weed management. The development of these algorithms in free and open‐source software fosters their application in small and medium farms. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. The use of imagery from a low‐cost unmanned aerial vehicle and open‐source algorithms allow mapping Ecballium elaterium infestations in hedgerow olive orchards for site‐specific weed management.