Defining and Evaluating a Decision Support System (DSS) for the Precise Pest Management of the Mediterranean Fruit Fly, Ceratitis capitata, at the Farm Level

Sciarretta, Andrea; Tabilio, M. R.; Amore, A.; Colacci, Marco; Miranda, Miguel Ángel; Nestel, David; Papadopoulos, Nikos T.; Trematerra, Pasquale

doi:10.3390/agronomy9100608

Cited by 17 publications

(6 citation statements)

References 20 publications

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“…The e-traps are introduced in automatic [4]- [16] and semiautomatic system [25]- [28]. The e-trap is a trap that is equipped with a camera, a wireless transceiver and a controller.…”

Section: Related Workmentioning

confidence: 99%

YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting

Mamdouh

Khattab

2021

IEEE Access

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The olive fruit fly can damage up to 100% of the harvested fruit and can cause up to 80% reduction of the value of the resulting olive oil. Therefore, it is important to early detect its presence in the olive orchard to take the appropriate chemical or biological countermeasures as early as possible. Traps filled with attractant pheromones are typically deployed across the orchard to attract and capture the flies. Traditionally, the captured flies were manually counted which is error prone. Recently, the traps are employed with cameras and communication devices to send pictures of the captured flies to experts for analysis which is also error prone and inefficient. Consequently, machine and deep learning have been exploited to develop fully automated and accurate detection that does not include human in the loop. Such a learning problem is challenging due to the small size of the detected object, the differences in the light conditions at which pictures were taken, and the lack of enough data to train the learning model. In this paper, we present a deep learning framework for detecting and counting the number of olive fruit flies that exploits data augmentation to increase the dataset size, includes negative samples in the training to improve the detection accuracy, and normalizes the images to the color of the trap background, i.e., yellow, to unify the illumination conditions. The results of the proposed framework show a precision of 0.84, a recall of 0.97, an F1-score of 0.9 and mean Average Precision (mAP) of 96.68% which significantly outperforms existing pest detection systems.

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“…The e-traps are introduced in automatic [4]- [16] and semiautomatic system [25]- [28]. The e-trap is a trap that is equipped with a camera, a wireless transceiver and a controller.…”

Section: Related Workmentioning

confidence: 99%

YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting

Mamdouh

Khattab

2021

IEEE Access

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“…The Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) is as a highly polyphagous pest that can infest more than 300 different hosts and is considered one of the most threatening pests of the fruit producing industry worldwide [15,16]. Because of its economic importance, there are intensive efforts to develop effective control methods and strategies to manage medfly populations [17][18][19]. For example, the sterile insect technique (SIT) has been extensively applied against medfly [20].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Brewer’s Yeast Quantity in Liquid and Gel Larval Diets for the Mediterranean Fruit Fly

Prekas,

Rodovitis,

Bataka

et al. 2023

Insects

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Several artificial larval diets have been developed, evaluated and used for mass-rearing of the Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann) (Diptera: Teprhitidae). There are several efforts to reduce the cost of rearing and optimize the quality of the produced sterile males that are destined for release in sterile insect release programs. Survival, growth, longevity and reproductive capacity of sterile males are strongly connected with the most expensive ingredient, the brewer’s yeast (protein), in the larval diet. The current study focused on settling the optimal content of brewer’s yeast in a liquid diet and a gel diet. Egg hatch rates, developmental duration of immatures, pupation rate, pupae and adult survival were recorded as indicators of quantity and quality of the produced adults. Egg hatch was higher and larval developmental duration longer in the gel diet. In contrast to the liquid diet, an increase in brewer’s yeast concentration was correlated with increased pupation rate and pupae survival in the gel diet. Reducing brewer’s yeast up to 50% of its initial quantity had no significant effect on the survival of the emerging adults regardless of the diet type. Our findings may contribute to the production of low-cost and effective diets for use in mass-rearing facilities of medflies.

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“…Automation of agricultural processes and decision making in agriculture has advanced at enormous steps during the last decade (Jung et al 2021). Within these advances, automation of insect-monitoring and decision-making in pest management has been amply developed, and novel instruments (i.e., sensors), principles and agricultural applications were recently developed and communicated (e.g., Deqin et al 2016;Goldshtein et al 2017;Potamitis et al 2018;Ioannou et al 2019;Miranda et al 2019;Nestel et al 2019;Sciarretta et al 2019;Cardim Ferreira Lima et al 2020;Preti et al 2021;Schellhorn and Jones 2021). Moreover, some of these electronic traps have achieved commercialization, (see for instance the RapidAIM, https:// rapid aim.…”

Section: Introductionmentioning

confidence: 99%

“…Other type of sensors include those based on the acquisition of images from the insect pest (Preti et al 2021). Prototypes of these smart traps used to monitor FF include, within others, those tested for the oriental fruit fly, Bactrocera dorsalis (Deqin et al 2016), the Ethiopian fruit fly, Dacus ciliatus (Nestel et al 2019), the Mediterranean fruit fly (medfly), Ceratitis capitata (Sciarretta et al 2019) and the olive fruit fly, B. oleae (Miranda et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis

et al. 2022

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Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitata, Bactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.

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Defining and Evaluating a Decision Support System (DSS) for the Precise Pest Management of the Mediterranean Fruit Fly, Ceratitis capitata, at the Farm Level

Cited by 17 publications

References 20 publications

YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting

YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting

Optimization of Brewer’s Yeast Quantity in Liquid and Gel Larval Diets for the Mediterranean Fruit Fly

A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis

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