Sampling of Bemisia tabaci adults using a pre-programmed autonomous pest control robot

Chung, Bu-Keun; Xia, Chunlei; Song, Yeong Hun; Lee, Jang-Myung; Yan, Li; Kim, Hung-Soo; Chon, Tae‐Soo

doi:10.1016/j.aspen.2014.06.009

Cited by 15 publications

(16 citation statements)

References 22 publications

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“…Therefore, future researchs should to be oriented towards new methodologies to make fieldwork easier and more efficient. For instance, important advances in entomology field have been achieved using autonomous pest control robots (see Chung et al 2014) and remote sensing by satellite imaging or unmanned aerial vehicles (see Nansen and Elliott 2016).…”

Section: Resultsmentioning

confidence: 99%

Sampling grids for monitoring Euschistus heros infestation in soybean

et al. 2017

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Section: Resultsmentioning

confidence: 99%

Sampling grids for monitoring Euschistus heros infestation in soybean

et al. 2017

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“…In total, fifteen sticky traps were selected for collecting species for one week. Methods for plant cultivation and insect observation were reported in (Chung et al, 2014;Qiao et al, 2008). After field collection, sticky trap images were acquired by the HP G3110® scanner.…”

Section: Methodsmentioning

confidence: 99%

Automatic identification and counting of small size pests in greenhouse conditions with low computational cost

Xia

Chon

Ren

et al. 2015

Ecological Informatics

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a b s t r a c t a r t i c l e i n f oWe propose an automatic pest identification method suitable for large scale, long term monitoring for mobile or embedded devices in situ with less computational cost. A procedure of segmentation and image separation was devised to identify common greenhouse pests, whiteflies, aphid and thrips. Initially, the watershed algorithm was used to segment insects from the background (i.e., sticky trap) images. Color feature of the insects were subsequently extracted by Mahalanobis distance for identification of pest species. Accuracy and computational costs were evaluated across different image resolutions. The correlation of determination (R 2 ) between the proposed identification scheme and manual identification were high, showing 0.934 for whitefly, 0.925 for thrips, and 0.945 for aphids even with low resolution images. Comparing with the conventional methods, pests were efficiently identified with low computational cost. Optimal image resolution for species identification regarding long-term survey was discussed in practical aspect with less computational complexity.

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“…Xie et al employed the hyper-spectral images to detect whether there was gray mold disease in tomato leaves [6]. In addition, the researchers constructed an automated detection and monitoring system for the detection of small pests in the greenhouse, such as whitefly, etc., which can effectively monitor the tiny insects and their densities [7][8][9][10]. Meanwhile, computer vision technology was also used for aphid detection and monitoring of its population [11].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Robot for Intelligent Detection of Pyralidae Insects

Hu¹,

Liu²,

Zhao³

2019

Agricultural Robots - Fundamentals and Applications

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The Pyralidae insects are one of the main pests in economic crops. However, the manual detection and identification of Pyralidae insects are labor intensive and inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an insect monitoring robot and a new method to recognize the Pyralidae insects are presented in this chapter. Firstly, the robot gets images by performing a fixed action and detects whether there are Pyralidae insects in the images. The recognition method obtains the total probability image by using reverse mapping of histogram and multi-template images, and then image contour can be extracted quickly and accurately by using constraint Otsu. Finally, according to the Hu moment characters, perimeter, and area characters, the contours can be filtrated, and recognition results with triangle mark can be obtained. According to the recognition results, the speed of the robot car and mechanical arm can be adjusted adaptively. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene.

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Sampling of Bemisia tabaci adults using a pre-programmed autonomous pest control robot

Cited by 15 publications

References 22 publications

Sampling grids for monitoring Euschistus heros infestation in soybean

Sampling grids for monitoring Euschistus heros infestation in soybean

Automatic identification and counting of small size pests in greenhouse conditions with low computational cost

Agricultural Robot for Intelligent Detection of Pyralidae Insects

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