Remote fruit fly detection using computer vision and machine learning-based electronic trap

Miguel Molina-Rotger; Alejandro Morán; Miguel Angel Miranda; Bartomeu Alorda-Ladaria

doi:10.3389/fpls.2023.1241576

Remote fruit fly detection using computer vision and machine learning-based electronic trap

Front Plant Sci. 2023 Oct 10:14:1241576. doi: 10.3389/fpls.2023.1241576. eCollection 2023.

Authors

Miguel Molina-Rotger¹, Alejandro Morán¹, Miguel Angel Miranda^{2

3}, Bartomeu Alorda-Ladaria^{1

3

4}

Affiliations

¹ Industrial Engineering and Construction Department, University of the Balearic Islands, Palma, Spain.
² Biology Department, University of the Balearic Islands, Palma, Spain.
³ Institute for Environmental Agro-Environmental Research and Water Economics, University of the Balearic Islands, Palma, Spain.
⁴ Health Science and Technology Cross-cutting Department, Balearic Islands Health Research Institute (IdISBa), Palma, Spain.

Abstract

Introduction: Intelligent monitoring systems must be put in place to practice precision agriculture. In this context, computer vision and artificial intelligence techniques can be applied to monitor and prevent pests, such as that of the olive fly. These techniques are a tool to discover patterns and abnormalities in the data, which helps the early detection of pests and the prompt administration of corrective measures. However, there are significant challenges due to the lack of data to apply state of the art Deep Learning techniques.

Methods: This article examines the detection and classification of the olive fly using the Random Forest and Support Vector Machine algorithms, as well as their application in an electronic trap version based on a Raspberry Pi B+ board.

Results: The combination of the two methods is suggested to increase the accuracy of the classification results while working with a small training data set. Combining both techniques for olive fly detection yields an accuracy of 89.1%, which increases to 94.5% for SVM and 91.9% for RF when comparing all fly species to other insects.

Discussion: This research results reports a successful implementation of ML in an electronic trap system for olive fly detection, providing valuable insights and benefits. The opportunities of using small IoT devices for image classification opens new possibilities, emphasizing the significance of ML in optimizing resource usage and enhancing privacy protection. As the system grows by increasing the number of electronic traps, more data will be available. Therefore, it holds the potential to further enhance accuracy by learning from multiple trap systems, making it a promising tool for effective and sustainable fly population management.

Keywords: computer vision; edge computing; machine learning; olive fruit fly pest; precision agriculture; random forest; remote sensing; support vector machine.

Grants and funding

This work has been partially sponsored and promoted by the Comunitat Autonoma de les Illes Balears through the Direcció General de Recerca, Innovació I Transformació Digital and the Conselleria de Economia, Hisenda I Innovació and by the European Union- Next Generation UE (BIO/016 A.2). Nevertheless, the views and opinions expressed are solely those of the author or authors, and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission are to be held responsible.