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- W3036502638 abstract "Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research." @default.
- W3036502638 created "2020-06-25" @default.
- W3036502638 creator A5034412369 @default.
- W3036502638 date "2020-06-24" @default.
- W3036502638 modified "2023-10-18" @default.
- W3036502638 title "Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review" @default.
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- W3036502638 doi "https://doi.org/10.3390/ai1020021" @default.
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