Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

Arapostathi, Evaggelia; Panopoulou, Christina; Antonopoulos, Athanasios; Katsileros, Anastasios; Karellas, Konstantinos; Dimopoulos, Christos; Tsagkarakis, Antonios

doi:10.3390/agronomy14010020

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…For instance, Marin et al [37] studied the ripeness of Moriche palm fruit using UAV-based multispectral images, tracking changes in reflectance at various wavelengths across the phenological cycle. Similarly, Arapostathi et al [38] estimated the yield of a single apple tree using UAV multispectral technology, all while focusing on spectral data acquired at a single relative azimuth angle. In contrast, this study collected multispectral data from multiple relative azimuth angles, increasing the dimensionality of input variables and ultimately enhancing the accuracy of the prediction models' results.…”

Section: Discussionmentioning

confidence: 99%

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Ma,

Wang,

Luo

et al. 2024

Applied Sciences

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To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.

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Section: Discussionmentioning

confidence: 99%

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Ma,

Wang,

Luo

et al. 2024

Applied Sciences

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Capnodis tenebrionis (Coleoptera: Buprestidae), an important pest of stone fruits in the Mediterranean basin: current management strategies and prospects for integrated pest management

Nasouri

2024

Journal of Integrated Pest Management

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Capnodis tenebrionis (Linné) is a devastating pest of stone fruits (Prunus spp.) in the Mediterranean region. The endophytic root-boring larvae cause the main damage and can kill a large tree within 2 years. For several decades, with the absence of an effective biological control strategy, the management of this pest has mainly relied on the use of nonselective insecticides. These insecticides are applied either as a foliar spray targeting adults or as a soil treatment targeting neonate larvae. The search for alternative management options has increased since 2000 as a result of reduced efficacy of chemical control, decreased number of available insecticides, and the need for control measures suitable for organic stone fruit production. The main focus was on entomopathogenic nematodes and fungi. Several isolates/strains of these pathogens were found to be effective against larvae and adults of C. tenebrionis under laboratory and semi-field conditions. In this article, we review the current management options of C. tenebrionis, including chemical, biological, resilient rootstock, and cultural options. The prospects for developing an integrated management approach for this pest are also discussed.

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CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement

Liu,

He,

Wang

et al. 2024

Plants

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Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer from light and noise in scenarios with dense small target clusters and extreme light. To solve these problems, this study proposes a multimodal detector, called CRLNet, based on RGB and depth images. First, YOLOv9 was extended to design a backbone network that can extract RGB and depth features in parallel from an image. Second, to address the problem of information fusion bias, the Rough–Fine Hybrid Attention Fusion Module (RFAM) was designed to combine the advantageous information of different modes while suppressing the hollow noise at the edge of the peach. Finally, a Transformer-based Local–Global Joint Enhancement Module (LGEM) was developed to jointly enhance the local and global features of peaches using information from different modalities in order to enhance the percentage of information about the target peaches and remove the interference of redundant background information. CRLNet was trained on the Peach dataset and evaluated against other state-of-the-art methods; the model achieved an mAP50 of 97.1%. In addition, CRLNet also achieved an mAP50 of 92.4% in generalized experiments, validating its strong generalization capability. These results provide valuable insights for peach and other outdoor fruit multimodal detection.

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Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

Cited by 3 publications

References 45 publications

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Capnodis tenebrionis (Coleoptera: Buprestidae), an important pest of stone fruits in the Mediterranean basin: current management strategies and prospects for integrated pest management

CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement

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