Smart insect monitoring based on YOLOV5 case study: Mediterranean fruit fly Ceratitis capitata and Peach fruit fly Bactrocera zonata

Article Agronomy

EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard

Dana Cirjak et al.

Summary: This study aims to develop an automatic monitoring system for the codling moth, a damaging apple pest, based on deep neural networks. The system consists of a smart trap and an analytical model, which enables on-site data processing and transmission of detection results to users. The developed model shows an accuracy of over 99% in detecting codling moths, making it significant for automated pest monitoring and sustainable apple production.

AGRICULTURE-BASEL (2023)

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Article Entomology

Spectroradiometer and thermal imaging as tools from remote sensing used for early detection of spiny bollworm, Earias insulana (Boisd.) infestation

Mona M. El Hoseny et al.

Summary: This study aims to detect the presence of spiny bollworm in cotton bolls using spectroscopy and thermal imaging techniques for early warning and management. The results of the study indicate that spectral measurement and thermal imaging can effectively detect the presence of spiny bollworm, and this method may also be effective for pest detection in other crops.

INTERNATIONAL JOURNAL OF TROPICAL INSECT SCIENCE (2023)

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Article Remote Sensing

1 Unsupervised hyperspectral band selection with deep autoencoder unmixing

Menna M. Elkholy et al.

Summary: This paper proposes a new unsupervised band selection approach that leverages deep learning frameworks, with two consecutive phases to select the optimal band subset. Experimental results show that the proposed approach outperforms several state-of-the-art counterparts in terms of accuracy.

INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION (2022)

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Proceedings Paper Computer Science, Information Systems

A Review of Yolo Algorithm Developments

Peiyuan Jiang et al.

Summary: This paper provides a brief overview of the YOLO algorithm and its subsequent advanced versions, highlighting the ongoing improvement of the algorithm. The analysis reveals the differences and similarities among different YOLO versions and between YOLO and CNNs.

8TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2020 & 2021): DEVELOPING GLOBAL DIGITAL ECONOMY AFTER COVID-19 (2022)

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Proceedings Paper Computer Science, Artificial Intelligence