A survey of deep learning techniques for weed detection from images

Hasan, Aamir; Sohel, Ferdous; Diepeveen, Dean; Laga, Hamid; Jones, Michael G. K.

doi:10.1016/j.compag.2021.106067

Cited by 276 publications

(196 citation statements)

References 102 publications

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“…Recent research studies focused on DL models [ 38 , 39 ] involve just the training of deep learning models for weed detection. However, this study focuses not only on training the DL model but also on the implementation process via the deployment of DL on a UAV that could be used to access hard-to-reach areas.…”

Section: Resultsmentioning

confidence: 99%

Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture

Ukaegbu

Tartibu

Okwu

et al. 2021

Sensors

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This paper describes the development of a modular unmanned aerial vehicle for the detection and eradication of weeds on farmland. Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the problematic weeds using emerging technologies. This research has addressed the aforementioned problem. A quadcopter was built, and components were assembled with light-weight materials. The system consists of the electric motor, electronic speed controller, propellers, frame, lithium polymer (li-po) battery, flight controller, a global positioning system (GPS), and receiver. A sprayer module which consists of a relay, Raspberry Pi 3, spray pump, 12 V DC source, water hose, and the tank was built. It operated in such a way that when a weed is detected based on the deep learning algorithms deployed on the Raspberry Pi, general purpose input/output (GPIO) 17 or GPIO 18 (of the Raspberry Pi) were activated to supply 3.3 V, which turned on a DC relay to spray herbicides accordingly. The sprayer module was mounted on the quadcopter and from the test-running operation conducted, broadleaf and grass weeds were accurately detected and the spraying of herbicides according to the weed type occurred in less than a second.

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Section: Resultsmentioning

confidence: 99%

Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture

Ukaegbu

Tartibu

Okwu

et al. 2021

Sensors

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“…The specific calculation formula needs to check the current work is classification recognition or semantic segmentation. Researchers could refer to the review written by Hasan et al [ 12 ], which described 23 evaluation metrics by different researchers of the related works.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

“…Nonetheless, few discussions were presented about the use of deep learning methods to solve the problem of weed identification. Hasan et al [ 12 ] provided a comprehensive review of weed detection and classification research but focused on methods based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

148

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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“…Convolutional neural networks are one of the most widely used deep supervised learning models in a wide spectrum of remote sensing applications and have achieved extraordinary improvement in recent years in the classification of remotely sensed data [52,53]. The use of diverse CNNs in crops and plant phenology recognition [54][55][56][57][58][59], weed detection [60][61][62], agriculture [51,63], vegetation mapping [64][65][66][67][68], tree crown detection and mapping [69][70][71][72], and disease detection [73][74][75][76][77] has elicited considerable interest.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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A survey of deep learning techniques for weed detection from images

Cited by 276 publications

References 102 publications

Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture

Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture

Review of Weed Detection Methods Based on Computer Vision

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

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