EyesOnTraps: AI-Powered Mobile-Based Solution for Pest Monitoring in Viticulture

Luís Rosado (), Pedro Faria, João Gonçalves, Eduardo Silva, Ana Vasconcelos, Cristiana Braga, João Oliveira, Rafael Gomes, Telmo Barbosa, David Ribeiro, Telmo Nogueira, Ana Ferreira and Cristina Carlos
Additional contact information
Luís Rosado: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Pedro Faria: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
João Gonçalves: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Eduardo Silva: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Ana Vasconcelos: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Cristiana Braga: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
João Oliveira: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Rafael Gomes: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Telmo Barbosa: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
David Ribeiro: Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Telmo Nogueira: GeoDouro—Consultoria e Topografia, Lda., 5100-196 Lamego, Portugal
Ana Ferreira: Associação para o Desenvolvimento da Viticultura Duriense, 5000-033 Vila Real, Portugal
Cristina Carlos: Associação para o Desenvolvimento da Viticultura Duriense, 5000-033 Vila Real, Portugal

Sustainability, 2022, vol. 14, issue 15, 1-18

Abstract: Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and intrinsic difficulties in performing pest monitoring. Conventional pest monitoring programs consist of deploying sticky traps on vineyards, which attract key insects and allow human operators to identify and count them manually. However, this is a time-consuming process that usually requires in-depth taxonomic knowledge. This scenario motivated the development of EyesOnTraps, a novel AI-powered mobile solution for pest monitoring in viticulture. The methodology behind the development of the proposed system merges multidisciplinary research efforts by specialists from different fields, including informatics, electronics, machine learning, computer vision, human-centered design, agronomy and viticulture. This research work resulted in a decision support tool that allows winegrowers and taxonomy specialists to: (i) ensure the adequacy and quality of mobile-acquired sticky trap images; (ii) provide automated detection and counting of key insects; (iii) register local temperature near traps; and (iv) improve and anticipate treatment recommendations for the detected pests. By merging mobile computing and AI, we believe that broader technology acceptance for pest management in viticulture can be achieved via solutions that work on regular sticky traps and avoid the need for proprietary instrumented traps.

Keywords: viticulture; pests monitoring; insect traps; machine learning; artificial intelligence; mobile devices (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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