Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning

Domingues, Tiago; Brandão, Tomás; Ribeiro, Ricardo; Ferreira, João

doi:10.3390/agriculture12111967

Cited by 9 publications

(3 citation statements)

References 80 publications

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“…Our study yields similar results in this regard. Domingues et al (2022) detected certain pests (such as bemisia tabaci, helicoverpa armigera) using sticky traps with YOLOv5, reaching a 94.4% mAP_0.5, with a precision and recall of 88% and 91%, respectively, using YOLOv5x. In their experiment, Zhang et al (2023) observed six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies.…”

Section: Analysis Of Training Yolov5s V5m V5l Resultsmentioning

confidence: 99%

Detection of the Metcalfa pruinosa (Hemiptera: Flatidae) pest on the Jujube plant (Ziziphus jujuba) using a sequence of YOLOv5 models

Erdinç,

Erdoğan

2024

Türk Tarım Ve Doğa Bilimleri Dergisi

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This study aimed to detect the adult of the pest Metcalfa pruinosa observed on jujube plants using the YOLOv5 algorithm's v5s, v5m, and v5l models. Performance metrics, including box_loss, obj_loss, precision, recall, mAP_0.5, and mAP_0.5:0.95, were observed in the research. In the YOLOv5s model, the box_loss and obj_loss performance metrics were found to be the highest, with values of 0.02858 and 0.0055256, respectively. In the YOLOv5m model, the recall performance metric was identified as the highest, with a value of 0.98127. In the YOLOv5l model, precision, mAP_0.5, and mAP_0.5:0.95 performance metrics were identified as the highest, with values of 0.98122, 0.99500, and 0.67864, respectively. Consequently, the YOLOv5l model exhibits higher precision compared to others. It is believed that the YOLOv5l model is sufficient for the detection of the Metcalfa pruinosa pest.

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Section: Analysis Of Training Yolov5s V5m V5l Resultsmentioning

confidence: 99%

Detection of the Metcalfa pruinosa (Hemiptera: Flatidae) pest on the Jujube plant (Ziziphus jujuba) using a sequence of YOLOv5 models

Erdinç,

Erdoğan

2024

Türk Tarım Ve Doğa Bilimleri Dergisi

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“…Automatic image identification technologies based on computer vision are promising in insect detection, as documented in the literature (Ahmad et al, 2022;Júnior and Rieder, 2020;Zacarés et al, 2018). They have been implemented in numerous applications in managing insect disease vectors and controlling pests, such as agricultural and forest pests (Domingues et al, 2022;Duarte et al, 2022;Mendoza et al, 2023), in the classification of parasitised fruit fly pupae (Marinho et al, 2023), the detection of pine pests (Ye et al, 2022), the segmentation of ecological images featuring (Filali et al, 2022), the identification of whitefly (Kamei, 2023), and the automated counting of mosquito eggs (Javed et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Monitoring the spread of a pathogenic insect on vineyards using UAS

Longhi,

Martino,

Lingua

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.

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“…A specific example of a sensing device is the automated insect trap which is used in pest management. Automated traps are essential for keeping track of insect activity and are frequently used in pest population forecasting [2]. Images taken by the traps carry a huge amount of data and sophisticated data analysis methods are needed to extract the necessary information which can be used for decision making.…”

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confidence: 99%