Weed Seedling Detection Using Mask Regional Convolutional Neural Network

Patidar, Sanjay; Singh, Utkarsh; Sharma, Sumit; Himanshu, -

doi:10.1109/icesc48915.2020.9155701

Cited by 20 publications

(10 citation statements)

References 6 publications

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“…The research results showed that the data balance and better spatial semantic information made the experimental results more accurate. Patidar et al [ 129 ] proposed an improved Mask RCNN model to extract early cranesbill seedlings. These weeds can be used as herbal medicines for rheumatic disease.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

171

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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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Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

171

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“…Huang et al, 2018c;H. Huang et al, 2020;Petrich et al, 2019) or multispectral images (Osorio et al, 2020;Patidar et al, 2020;Ramirez et al, 2020;Sa et al, 2017;Sa et al, 2018). In addition, UAVs can be used to identify crop rows and map weeds within crop rows by collecting RGB (Red, Green and Blue color) images (Bah et al, 2018).…”

Section: Paper Selection Criteria In This Surveymentioning

confidence: 99%

“…Abdalla et al (2019),Adhikari et al (2019),Andrea et al (2017),Asad and Bais (2019),Bini et al (2020),Bosilj et al (2020),Brilhador et al (2019),Chechlinski et al (2019), Farooq et al (2019), Fawakherji et al (2019), Hall et al (2018, H Huang et al (2018a), H. Huang et al (2018b,. H Huang et al (2020),Ishak et al (2007),Knoll et al (2019),Kounalakis et al (2019),Lam et al (2020),Liakos et al (2018),Lottes et al (2020),Lottes et al (2018b), Milioto et al (2017,Osorio et al (2020),Patidar et al (2020),Ramirez et al (2020),Rist et al (2019), andSa et al (2018),Skovsen et al (2019),Umamaheswari and Jain (2020)…”

mentioning

confidence: 99%

A Survey of Deep Learning Techniques for Weed Detection from Images

Hasan¹,

Sohel²,

Diepeveen³

et al. 2021

Preprint

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The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos. DL techniques are now being used in many applications related to agriculture and farming. Automatic detection and classification of weeds can play an important role in weed management and so contribute to higher yields. Weed detection in crops from imagery is inherently a challenging problem because both weeds and crops have similar colours ('green-on-green'), and their shapes and texture can be very similar at the growth phase. Also, a crop in one setting can be considered a weed in another. In addition to their detection, the recognition of specific weed species is essential so that targeted controlling mechanisms (e.g. appropriate herbicides and correct doses) can be applied. In this paper, we review existing deep learning-based weed detection and classification techniques. We cover the detailed literature on four main procedures, i.e., data acquisition, dataset preparation, DL techniques employed for detection, location and classification of weeds in crops, and evaluation metrics approaches. We found that

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“…Authors Ramirez et al in [7] compared with SegNet and U-Net for aerial image weed segmentation model. In [8], an enhanced method called Region based CNN model to extract early harebell seedlings has been presented. The presented method allowed the weeds to be fully identified from the exact input in order to obtain nutrients from the crops.…”

Section: Literature Surveymentioning

confidence: 99%

Development of a Novel Classification technique for detection of Cowpea Leaves using VGG16 Deep Convolutional Network

Choudhary

Guha

Tripathi

et al. 2021

Preprint

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This paper is based on CNN-VGG16 network architecture in order to identify the cowpea plants from weed in the cowpea fields. It is done to increase the productivity of the crops. The VGG16 is a basic network of Convolutional Neural Network family and it has quite good classification performance. Implement and modification is quite easy with this model. Here, we have applied pre-trained VGG16 method to the cowpea as well as weed leaves custom dataset. Images of leaves have been taken from standard research farm, ICAR-NBPGR, Pusa campus, New Delhi. The dataset includes 1230 images of two classes. The basic principle of VGG16 network for automatic detection includes, feature extraction from input image, addition of convo layer, pooling layer addition, flatten (fully connected layer), and sigmoid for classifiers. Results from experiments show that the model efficiently classifies the cowpea and weed images. Here, accuracy rate on the training dataset is 97.58%, and that of test dataset set is 90.08%. Such results give hope for achieving agricultural reforms.

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Weed Seedling Detection Using Mask Regional Convolutional Neural Network

Cited by 20 publications

References 6 publications

Review of Weed Detection Methods Based on Computer Vision

Review of Weed Detection Methods Based on Computer Vision

A Survey of Deep Learning Techniques for Weed Detection from Images

Development of a Novel Classification technique for detection of Cowpea Leaves using VGG16 Deep Convolutional Network

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