Image processing for distance diagnosis in pest management

Koumpouros, Yiannis; Mahaman, B.D.; Maliappis, Michael T.; Passam, H. C.; Sideridis, Alexander B.; Zorkadis, Vasilios

doi:10.1016/j.compag.2004.04.004

Cited by 25 publications

(10 citation statements)

References 5 publications

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“…Also, numerous researches equally acknowledged the scarcity of works on expert systems for diagnosing and managing the pests and diseases for a certain crop (Yialouris and Sideridis, 1996;Mahaman et al, 2003;Clark et al, 1991;Koumpouros, 2004). In addition to this, most researches still focus on adoption of only one artificial technique for development of expert system for diagnosis of nutritional disorders in crops like tomato, soyabean, cassava, rice etc.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neuro-Fuzzy System For Diagnosis of Soya-Beans Diseases

Rahmon¹,

Akinsanya²,

Eze³

2018

RJMCS

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Section: Literature Reviewmentioning

confidence: 99%

A Neuro-Fuzzy System For Diagnosis of Soya-Beans Diseases

Rahmon¹,

Akinsanya²,

Eze³

2018

RJMCS

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“…An alternative solution may be to transmit the field images to the remote terminal (e.g., personal computer) for pest identification. But the transmission ability of the wireless network would be still a constraining factor since high resolution images would require a broadband network of which the transmission range is limited, especially in the large scale agricultural field (Koumpouros et al, 2004). Although recent 3G/4G mobile network may provide a technical solution to the transmission of images in large scale (i.e., data transmission between cites), the cost of the mobile network would not be affordable for agricultural practices by dealing with high resolution data.…”

Section: Introductionmentioning

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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“…Pest dispersal is critical, especially in a greenhouse environment, considering that the plants are cultivated in highly condensed concentrations in a closed, homogeneous environment [2]. Efficient control of pests is desired for the proper economic management of agricultural practices.…”

Section: Introductionmentioning

confidence: 99%