Image-based orchard insect automated identification and classification method

Title Image-based orchard insect automated identification and classification methodTitle (native language)

Category Recording or mapping technology

Short summary forpractitioners (Practiceabstract) in English)

An image-based automated insect identification and classification method is described in the paper.The complete method includes three models. An invariant local feature model was built for insectidentification and classification using affine invariant local features; a global feature model was builtfor insect identification and classification using 54 global features; and a hierarchical combinationmodel was proposed based on local feature and global feature models to combine advantages of thetwo models and increase performance. The three models were applied and tested for insectclassification on eight insect species from pest colonies and orchards. The hierarchical combinationmodel yielded better performance over global and local models. Moreover, to study the pose change ofinsects on traps and the hypothesis that an optimal time to acquire and image after landing exists,advanced analysis on time-dependent pose change of insects on traps is included in this study.

Short summary forpractitionersWebsiteAudiovisual materialLinks to other websitesAdditional commentsKeywords Plant production and horticultureAdditional keywords Insect classification; Image processing; Integrated pest management; Global feature; Local featureGeographical location(NUTS) EU

Other geographicallocation Global

Cropping systems Tree cropsField operations Crop protectionSFT users Farmer | ContractorEducation level of users Secondary education | Apprenticeship or technical school education | University educationFarm size (ha) 0-2 | 2-10 | 10-50 | 50-100 | 100-200 | >500

Scientific article

Title Image-based orchard insect automated identification and classification method

Full citation Wen, C.; Guyer, D. (2012). Computers and Electronics in Agriculture,DOI:10.1016/j.compag.2012.08.008

Effects of this SFTProductivity (crop yield per ha) Some increaseQuality of product No effectRevenue profit farm income Some increaseSoil biodiversity No effectBiodiversity (other than soil) No effectInput costs No effectVariable costs No effectPost-harvest crop wastage No effectEnergy use No effectCH4 (methane) emission No effectCO2 (carbon dioxide) emission No effectN2O (nitrous oxide) emission No effectNH3 (ammonia) emission No effectNO3 (nitrate) leaching No effectFertilizer use No effectPesticide use No effectIrrigation water use No effectLabor time No effectStress or fatigue for farmer Some decreaseAmount of heavy physical labour No effectNumber and/or severity of personal injury accidents No effectNumber and/or severity of accidents resulting in spills property damage incorrectapplication of fertiliser/pesticides etc. No effect

Pesticide residue on product No effectWeed pressure No effectPest pressure (insects etc.) Some decreaseDisease pressure (bacterial fungal viral etc.) No effect

Information related to how easy it is to start using the SFTThis SFT replaces a tool or technology that is currently used. The SFT is better than thecurrent tool no opinion

The SFT can be used without making major changes to the existing system no opinionThe SFT does not require significant learning before the farmer can use it no opinionThe SFT can be used in other useful ways than intended by the inventor strongly agreeThe SFT has effects that can be directly observed by the farmer no opinionUsing the SFT requires a large time investment by farmer no opinionThe SFT produces information that can be interpreted directly no opinion

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This factsheet was generated on 2018-Apr-03 11:57:18.