Detection of broadleaf weeds growing in turfgrass with convolutional neural networks

Yu, Jialin; Sharpe, Shaun M.; Schumann, Arnold W.; Boyd, Nathan S.

doi:10.1002/ps.5349

Cited by 64 publications

(69 citation statements)

References 32 publications

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“…Another emerging method of weed detection is using machine learning algorithms to directly extract crop features and classify weeds or crops based on the automatically extracted features [23][24][25]. dos Santos Ferreira et al [26] used a convolutional neural network (CNN-AlexNet) to perform weed detection on soybean crop images collected using a drone, classified the weeds among grass and broadleaf, and applied the specific herbicide to detected weeds.…”

Section: Digital Image Sensorsmentioning

confidence: 99%

Weed Detection for Selective Spraying: a Review

2020

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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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Section: Digital Image Sensorsmentioning

confidence: 99%

Weed Detection for Selective Spraying: a Review

2020

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“…Selection may be semi‐automated but nearly always includes some manual elements, either on the part of the person compiling the data set or indirectly – often before the image is posted on the internet . In addition, images are preprocessed to some degree before being submitted to the CNN, either by applying automated tools or manual selection to pick out objects (weeds or insects of interest) in training pool images …”

Section: Deep Neural Networkmentioning

confidence: 99%

“…DL has been slow in coming to pest management per se , but it has begun to make itself felt in agriculture in general . Exemplary applications include insect pest identification, identifying weeds in crops, and differentiating between plant diseases . Such tasks are directly analogous to tasks DL tools were developed to solve, i.e.…”

Section: Introductionmentioning

confidence: 99%

Putting deep learning in perspective for pest management scientists

Clark

2020

Pest Management Science

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Deep learning' is causing rapid technological changes in many fields of science, and conjectures about its potential for transforming everyone's work and lives is a matter of great debate. Unfortunately, it is all too easy to apply it as a 'black box' tool with little consideration of its potential limitations, especially when the data it is being applied to is less than perfect. In this Perspective, I try to put deep learning into a broader mechanistic and historical context by showing how it relates to older forms of artificial intelligence; by providing a general explanation of how it operates; and by exploring some of the challenges involved in its implementation. Examples wherein it has been applied to pest management problems are provided to illustrate how the technology works and the challenges deep learning faces. At least in the near term, its biggest impact on agrochemical development seems likely to come in automating the tedious work involved in assessing agrochemical efficacy, but getting there will require major investments in building large, well-curated data sets to work from and in providing the expertise required to assess the resulting model predictions in real-world scenarios. Deep learning may also come to complement the machine learning methodologies already available for use in pesticide discovery and development, but it seems unlikely to supplant them.

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“…Currently, most UAV sprayers are designed to conduct broadcast aerial applications when operating in autonomous mode. However, recent advances in remote sensing for weed detection and real‐time weed identification using convolutional neural networks and machine learning have increased our ability to implement site‐specific weed management strategies . UAV sprayers have the potential to improve the efficiency of applications when specifically treating previously mapped weed patches.…”

Section: Introductionmentioning

confidence: 99%

“…However, recent advances in remote sensing for weed detection and real-time weed identification using convolutional neural networks and machine learning have increased our ability to implement site-specific weed management strategies. 18,19 UAV sprayers have the potential to improve the efficiency of applications when specifically treating previously mapped weed patches. For example, Huang et al 20 successfully developed a UAV spray system capable of completing autonomous spot sprays.…”

Section: Introductionmentioning

confidence: 99%

Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management

Hunter

Gannon

Richardson

et al. 2019

Pest Management Science

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BACKGROUND Unmanned aerial vehicles (UAVs) have been used in agriculture to collect imagery for crop and pest monitoring, and for decision‐making purposes. Spraying‐capable UAVs are now commercially available worldwide for agricultural applications. Combining UAV weed mapping and UAV sprayers into an UAV integrated system (UAV‐IS) can offer a new alternative to implement site‐specific pest management. RESULTS The UAV‐IS was 0.3‐ to 3‐fold more efficient at identifying and treating target weedy areas, while minimizing treatment on non‐weedy areas, than ground‐based broadcast applications. The UAV‐IS treated 20–60% less area than ground‐based broadcast applications, but also missed up to 26% of the target weedy area, while broadcast applications covered almost the entire experimental area and only missed 2–3% of the target weeds. The efficiency of UAV‐IS management practices increased as weed spatial aggregation increased (patchiness). CONCLUSION Integrating UAV imagery for pest mapping and UAV sprayers can provide a new strategy for integrated pest management programs to improve efficiency and efficacy while reducing the amount of pesticide being applied. The UAV‐IS has the potential to improve the detection and control of weed escapes to reduce/delay herbicide resistance evolution. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Detection of broadleaf weeds growing in turfgrass with convolutional neural networks

Cited by 64 publications

References 32 publications

Weed Detection for Selective Spraying: a Review

Weed Detection for Selective Spraying: a Review

Putting deep learning in perspective for pest management scientists

Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management

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