Automatic Pest Monitoring Systems in Apple Production under Changing Climatic Conditions

Čirjak, Dana; Miklečić, Ivana; Lemić, Darija; Kos, Tomislav; Živković, Ivana Pajač

doi:10.3390/horticulturae8060520

Cited by 19 publications

(11 citation statements)

References 144 publications

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“…However, manual confirmation is also a part of the customer service [18]. Cirjak et al [1] claimed that the Trapview standard model had a problem with detecting other objects than the target insect while Preti et al [61] observed that the system incorrectly detects morphologically similar insects. The original Z-Trap system is also not perfect.…”

Section: Discussion On Moth Counting Methodsmentioning

confidence: 99%

“…The apple is one of the most important fruits in the world due to its versatile use [1]. Moreover, it is easy to grow, store, and transport.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, one of the main control strategies against the codling moth is to track the pest population and apply insecticides at the time of the insect invasion [7]. In apple orchards, approximately 70% of insecticide treatments have been applied against codling moth [1]. In practice, pheromone-based traps are used to estimate the codling moth population in a given area [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review

Sütő

2022

Agriculture

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The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the hardware side, more camera-equipped embedded systems have been designed to remotely capture and upload pest trap images. From the software side, with the aid of machine vision and machine learning methods, traditional (manual) identification and counting can be solved by algorithm. With the appropriate combination of the hardware and software components, spraying can be accurately scheduled, and the crop-defending cost will be significantly reduced. Although automatic traps have been developed for more pest species and there are a large number of papers which investigate insect detection, a limited number of articles focus on the C. pomonella. The aim of this paper is to review the state of the art of C. pomonella monitoring with camera-equipped traps. The paper presents the advantages and disadvantages of automated traps’ hardware and software components and examines their practical applicability.

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Section: Discussion On Moth Counting Methodsmentioning

confidence: 99%

“…The apple is one of the most important fruits in the world due to its versatile use [1]. Moreover, it is easy to grow, store, and transport.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review

Sütő

2022

Agriculture

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“…Automation is needed in this area because it would be more effective in detecting insects in agriculture. To identify the pest, Apurva Sriwastwa et al [30] used a color-based image segmentation technique. Extensive simulation findings on different pest images demonstrate that the suggested method outperforms Otsu's technique and edge detection segmentation.…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Insect Detection With Fine-Tuning YOLOX

Doan

2023

ijmst

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With the aim of detecting insect pests at an early stage, there has been an increasing demand for insect pest detection and classification, particularly in large-scale setups. Therefore, the aim of this research is to introduce a new real-time pest detection technique using a deep convolutional neural network, which not only offers improved accuracy but also faster speed and less computational effort. The networks were constructed using various modern object detector models such as YOLOv4, YOLOv5, and YOLOX. Our proposed networks were evaluated on a standard large-scale insect pest dataset, IP102, as well as on our collected dataset, Insect10. The experimental results demonstrate that our system surpasses previous methods and achieves satisfactory performance with 84.84% mAP on the Insect10 dataset and 54.19% mAP on the IP102 dataset. Our system can deliver precise and real-time pest detection and identification for agricultural crops, enabling highly accurate end-to-end pest detection that can be applied in realistic farming scenarios.

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“…We experienced rapid development over the last decade in insect detection technologies. Automatic counting in IPM-related research was mainly obtained by traps installed with different types of sensors [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype

Flórián

Jósvai

Tóth

et al. 2023

Insects

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Monitoring insect populations is essential to optimise pest control with the correct protection timing and the avoidance of unnecessary insecticide use. Modern real-time monitoring practices use automatic insect traps, which are expected to be able to estimate the population sizes of pest animals with high species specificity. There are many solutions to overcome this challenge; however, there are only a few data that consider their accuracy under field conditions. This study presents an opto-electronic device prototype (ZooLog VARL) developed by us. A pilot field study evaluated the precision and accuracy of the data filtering using an artificial neural network(ANN) and the detection accuracy of the new probes. The prototype comprises a funnel trap, sensor-ring, and data communication system. The main modification of the trap was a blow-off device that prevented the escape of flying insects from the funnel. These new prototypes were tested in the field during the summer and autumn of 2018, detecting the daily and monthly flight of six moth species (Agrotis segetum, Autographa gamma, Helicoverpa armigera, Cameraria ohridella, Grapholita funebrana, Grapholita molesta). The accuracy of ANN was always higher than 60%. In the case of species with larger body sizes, it reached 90%. The detection accuracy ranged from 84% to 92% on average. These probes detected the real-time catches of the moth species. Therefore, weekly and daily patterns of moth flight activity periods could be compared and displayed for the different species. This device solved the problem of multiple counting and gained a high detection accuracy in target species cases. ZooLog VARL probes provide the real-time, time-series data sets of each monitored pest species. Further evaluation of the catching efficiency of the probes is needed. However, the prototype allows us to follow and model pest dynamics and may make more precise forecasts of population outbreaks.

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Automatic Pest Monitoring Systems in Apple Production under Changing Climatic Conditions

Cited by 19 publications

References 144 publications

Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review

Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review

Large-Scale Insect Detection With Fine-Tuning YOLOX

Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype

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