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Weed Detection for Selective Spraying: a Review

  • Agriculture Robotics (J Billingsley, Section Editor)
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Abstract

Purpose of Review

Weed detection systems are important solutions to one of the existing agricultural problems—unmechanized weed control. Weed detection also helps provide a means of reducing or eliminating herbicide use, mitigating agricultural environmental and health impact, and improving sustainability.

Recent Findings

Deep learning-based techniques are replacing traditional machine learning techniques to detect weeds in real time with the development of new models and increasing computational power. More hybrid machine learning models are emerging, utilizing benefits from different techniques. More large-scale crop and weed image datasets are available online now, and this provides more data and opportunities for researchers and engineers to join and contribute to this field.

Summary

This article provides a mini-review of all the different emerging and popular weed detection techniques for selective spraying, and summarizes the trends in this area in the past several years.

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Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Authors and Affiliations

  1. BioResource & Agricultural Engineering Department, Cal Poly State University, San Luis Obispo, CA, 93407, USA

    Bo Liu & Ryan Bruch

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  1. Bo Liu
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Correspondence to Bo Liu.

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Liu, B., Bruch, R. Weed Detection for Selective Spraying: a Review. Curr Robot Rep 1, 19–26 (2020). https://doi.org/10.1007/s43154-020-00001-w

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