Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks

Čirjak, Dana; Aleksi, Ivan; Miklečić, Ivana; Antolković, Ana Marija; Vrtodušić, Rea; Viduka, Antonio; Lemić, Darija; Kos, Tomislav; Živković, Ivana Pajač

doi:10.3390/agriculture13010067

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…In this work, the photo processing was done inside the trap, and only the detection results are sent to the user. Čirjak et al [56] proposed a similar system for monitoring another important lepidopteran apple pest (Leucoptera malifoliella (O. Costa, 1836)). The proposed system was also based on DNNs and achieved high detection accuracy (>98%).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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First record of Leucoptera malifoliella (O. Costa, 1836) (Lepidoptera: Lyonetiidae) in Tunisia

Soltani,

Rahmouni

2024

EPPO Bulletin

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The leaf blister moth, Leucoptera malifoliella is an invasive pest of apple and other Rosaceae species. It causes significant damage to apple leaves, threatening the production quality and the overall health of the tree. In this paper, we report the first record of L. malifoliella in Tunisia and give details of its distribution in apple orchards of central west Tunisia (Kasserine governorate). After signs of the pest were observed, cocoons were collected from the field and reared until adult eclosion. Identification of the species was based on observations of external morphology in the laboratory in addition to the field diagnosis based on larval damage on leaves. Leucoptera malifoliella was collected firstly in Foussana delegation, then in Sbeitla, Sbiba, Djedliane, Kasserine and Feriana delegations, showing that it is widely spread in the Kasserine governorate. This leaf miner was clearly identified as one of the most abundant pests in some localities. This paper describing the first record of L. malifoliella in central west Tunisia aims to raise awareness on the pest and encourage the collaboration between apple producers, researchers and extension services in the region to establish a programme of monitoring and managing the pest.

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Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

Liu

Wei

et al. 2023

Agriculture

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The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

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Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks

Cited by 4 publications

References 23 publications

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

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

First record of Leucoptera malifoliella (O. Costa, 1836) (Lepidoptera: Lyonetiidae) in Tunisia

Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

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