New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Dan Popescu; Alexandru Dinca; Loretta Ichim; Nicoleta Angelescu

doi:10.3389/fpls.2023.1268167

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Front Plant Sci. 2023 Nov 2:14:1268167. doi: 10.3389/fpls.2023.1268167. eCollection 2023.

Authors

Dan Popescu¹, Alexandru Dinca¹, Loretta Ichim¹, Nicoleta Angelescu²

Affiliations

¹ Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania.
² Faculty of Electrical Engineering, Electronics, and Information Technology, University Valahia of Targoviste, Targoviste, Romania.

Abstract

Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production.

Keywords: artificial neural networks; deep learning; image processing; insect detection; pest detection; precision agriculture.

Publication types

Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by HALY.ID project. HALY.ID is part of ERA-NET Co-fund ICT-AGRI-FOOD, with funding provided by national sources [Funding agency UEFISCDI, project number 202/2020, within PNCDI III] and co-funding by the European Union’s Horizon 2020 research and innovation program, Grant Agreement number 862665 ERA-NET ICT-AGRI-FOOD (HALY-ID 862671).