Early detection of the Mediterranean Fruit Fly, Ceratitis capitata (Wied.) in oranges using different aspects of remote sensing applications

Yones, Mona; Khdery, Ghada A.; Aboelghar, Mohamed; Kadah, Taher; Ma'moun, Shireen A.M.

doi:10.1016/j.ejrs.2023.08.002

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Females deposit eggs inside a wide range of economically important fruits, and the larvae then feed on the fruit pulp. The damage inflicted by this invasive insect significantly affects fruit production and international trade, 14,15 thus making its management crucial for protecting the agricultural industry and ensuring food security. The selection of a host and the subsequent oviposition are critical behaviors for the reproductive success of insects.…”

Section: Introductionmentioning

confidence: 99%

Nanoformulations from olive pomace to enhance the efficacy of hydroxytyrosol as a natural pest control agent

Falsini,

Rosi,

Urciuoli

et al. 2024

Environ. Sci.: Nano

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Section: Introductionmentioning

confidence: 99%

Nanoformulations from olive pomace to enhance the efficacy of hydroxytyrosol as a natural pest control agent

Falsini,

Rosi,

Urciuoli

et al. 2024

Environ. Sci.: Nano

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Integrating deep learning with non‐destructive thermal imaging for precision guava ripeness determination

Low,

Ong,

Sim

et al. 2024

J Sci Food Agric

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BACKGROUNDTo mitigate post‐harvest losses and inform harvesting decisions at the same time as ensuring fruit quality, precise ripeness determination is essential. The complexity arises in assessing guava ripeness as a result of subtle alterations in some varieties during the ripening process, making visual assessment less reliable. The present study proposes a non‐destructive method employing thermal imaging for guava ripeness assessment, involving obtaining thermal images of guava samples at different ripeness stages, followed by data pre‐processing. Five deep learning models (AlexNet, Inception‐v3, GoogLeNet, ResNet‐50 and VGGNet‐16) were applied, and their performances were systematically evaluated and compared.RESULTSVGGNet‐16 demonstrated outstanding performance, achieving average precision of 0.92, average sensitivity of 0.93, average specificity of 0.96, average F1‐score of 0.92 and accuracy of 0.92 within a training duration of 484 s.CONCLUSIONThe present study presents a scalable and non‐destructive approach for guava ripeness determination, contributing to waste reduction and enhancing efficiency in supply chains and fruit production. These initiatives align with environmentally friendly practices in agriculture. © 2024 Society of Chemical Industry.

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Thermal Imaging

Mohd Ali,

Hashim,

Maringgal

et al. 2024

Non-Invasive and Non-Destructive Methods for Food Integrity

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Early detection of the Mediterranean Fruit Fly, Ceratitis capitata (Wied.) in oranges using different aspects of remote sensing applications

Cited by 4 publications

References 31 publications

Nanoformulations from olive pomace to enhance the efficacy of hydroxytyrosol as a natural pest control agent

Nanoformulations from olive pomace to enhance the efficacy of hydroxytyrosol as a natural pest control agent

Integrating deep learning with non‐destructive thermal imaging for precision guava ripeness determination

Thermal Imaging

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