=Paper=
{{Paper
|id=Vol-2786/Paper25
|storemode=property
|title=Weed Species Identification in Different Crops Using Precision Weed Management: A Review
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper25.pdf
|volume=Vol-2786
|authors=Anand Muni Mishra,Vinay Gautam
|dblpUrl=https://dblp.org/rec/conf/isic2/MishraG21
}}
==Weed Species Identification in Different Crops Using Precision Weed Management: A Review==
https://ceur-ws.org/Vol-2786/Paper25.pdf
180
WEED SPECIES IDENTIFICATION IN DIFFERENT
CROPS USING PRECISION WEED MANAGEMENT: A
REVIEW
Anand Muni Mishra and Vinay Gautam
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab
Abstract
Agriculture plays a vital role in societies and requires research, planning, and execution. It is important to
research new trends, scientific methods, and boosters that can give a boost it. The farmer can reduce the
amount of workload using some technology which is enhancing the quality of cereal. It is important to
identify and growth estimation of weed using deep learning technology in the field of convolution neural
networks. This review paper is identifying different types of weeds which are harmful to crop and also
identify weed controlling mechanism. It is also useful for researchers to assimilate and clustered the
weeds using artificial intelligence techniques and machine learning techniques and to study existing
technology of weed detection, which is useful for a researcher can propose new techniques for weed
classification and detection. This review paper concise the development of weed detection and
classification using the most recent technologies in the field of artificial intelligence and image processing
techniques. Concretely, the four procedures, i.e., pre-processing, segmentation, feature extraction, and
classification is a part of weed detection and classification were presented in detail. Sooner or later,
demanding situations and answers furnished by researchers for weed class and detection inside the
subject, together with occlusion and overlap of leaves, varying lighting conditions, and specific growth
degrees, have been mentioned.
Keywords
Weed detection; Weed Classification; SVM; Deep Learning, CNN.
1. Introduction Explains that the use of artificial intelligence will increase
efficiency at each level of agriculture and also increase the
income of farmers along with the productivity of crops.
Crop1 production is an important component of
These techniques use ‘image recognition’ as an underlying
agriculture which is responsible for global food
technology through 'deep learning models'. The same is
management. It requires proper planning and
very crucial in the field of weed detection which will be
management. Therefore, it is important to invent new
very fruitful to take necessary steps to improve crop
trends, scientific methods, and boosters that give a
production. There are different varieties of weed that are
boost to crop production. One of the boosters in this
harmful to crop production and need to be detected in the
field is soil fertility and its management. The
early stage of growth. The growth of weeds within the
measurement of soil to the right amount of fertilizers or
crop will affect the basic resources such as water, soil,
fertilizers can ensure the best results. Information on
minerals, fresh air, sunlight, etc which is the basic need of
how to use fertilizer and how to improve the
the crop. In recent studies, it has been found that 35% of
productivity of grain can be readily available to farmers
crops destroyed just due to the growth of different types
at the right time. This is possible with the use latest
of weeds in the agriculture field. The main objective of this
technology and technique based on artificial intelligence
paper is to study different tools and techniques used by
(AI), machine learning (ML) and deep learning (DL), etc.
the authors to detect and classify weeds, which are
[Indian govt. Nitti Aayog in its discussion paper on
necessary for the assessment of weeds development.
'National Strategy for Artificial Intelligence
Several other computer-oriented techniques such as
'https://niti.gov.in/national strategy-artificial-
artificial intelligence, wireless sensor network, some other
intelligence on 4th June 2018]
techniques which improve the quality of agriculture for
research also help to researcher. These research papers
also briefly describe and maintain the biological method of
Weed control strategy such as computer vision technology
1
ISIC’21:International Semantic Intelligence Conference,February
implemented on the biological method of weed control.
25–27, 2021,New Delhi, India
EMAIL: anand.mishra@chitkara.edu.in (Anand Muni Mishra) For each method described with deficiencies recognized
ORCID: 0000-0002-2975-6982 (Anand Muni Mishra) results on insects and plant bacillus, and examples of, and
©️ 2020 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
capacity for, integration with biological manipulate. This
CEUR Workshop Proceedings (CEUR-WS.org) complete paper is sub-divided into different sections:
181
Section 2 describes related work of the concerned area. segmentation technique differentiate monocot and
The weed classification and control techniques are die cot weed. Huang, W. et al. [9] Sreelakshmi et
explained under section 3. Section 4, describes Materials al [10] examine 1119 plants 54 test 682 detection
and Methodology. Comparative work is given in section 5 and detection accuracy is 0.37% therefore, some
and Section 6 is the conclusive section. weeds are difficult to distinguish visually.
Therefore, the category approach insect the pixel-
wise object base detection using deep learning
2. Related Work VGG 16 FCN technique. Datta et al. [11]
demonstrated a framework to classify weed
images. Philipp Lotter et al. [12] also use pixel-
The recent study and research in the field of wise image segmentation photograph sequences
agriculture predict the yield of the crop is allows our system to robustly estimate a pixel-
affected by different factors. The weeds are the sensible on weed, furnished comparisons to other
foremost factor that could harm crop yield. today's tactics, and display that our device
Therefore, this is the most important task to appreciably improves the accuracy of weed
identify and control the weeds at the early stage segmentation with retraining of the model. Om
of weed growth. This section describes the Tiwari et al. [13] implements an automated
different types of weeds and their management approach for the detection of weeds like transfer
and control techniques. The weed and control learning technique reduce the time for
classifications are laid-down below: determining the weeds using pertained model
Yuewei Yang et al. [1] have suggested the using implemented on some weeds having better
positive enable technique find the exact location accuracy(90%).Heo Choon Ngo et al. [14]
of the object and get solution by an encoder- implement weed detection using color
decoder conventional neural network (CNN) were classification, using an automated image
used for fast weed identification of harmful plan classification system is designed using CNN which
like weeds. Further, Chechliński, Łukasz, and is distinguish between weeds and crops, also used
Barbara Siemiątkowska. et al. [2] suggested the robot Lego Mind storm EV3 which is directly
clustering methods like weeds segmentation and connected to the computer will spray weed
classification based on deep learning also explain directly into the area near or at which time the
the benefits, the loss has been discussed. Rasti, weeds have been detected. Discussed by compare
Pejman et al. [3] And Inkyu Sa 1,Marija Popovic et weed detection, deep learning, 10 and 50 meters
al. [4] discuss some techniques for identifying and and implement on machine learning but the image
detect the weed to increase crop production using taken with different space. S.Manvel G.Forero et
image processing. Reduce the weeds using al. [15]The machine learning technique was
automatic robot technique with semantic obtained 93.23% accuracy as compared to the
segmentation CNN (mobile), feature extraction, image processing method. Dyrmann and R. N.
and recognition. Aji, Wahyu, and Kamarul Hawari Jørgensen et al. [16] get critical analysis of weed
et al. [5] Briefly stated by this method exclusively image identification, in this research paper approx
classifies the weed using UAV imaginary and 17000 weeds images of the wheat crop, this data
transfer learning with FCN technology. Huang, has been collected by which ATV-mounted
Huasheng, Jizhong Deng et al. [6] drift the camera, for weed detection implement using fully
detection of broadleaf weed on various crops. In convolution neural network (FCNN).Nima Teimouri
the weed classification process, an algorithm like and Mads Dyrmann et al. [17] Completely focused
multistage scattering transformation was playing on weed growth and implement a deep
an important role, weed detection using convolution neural network (DCNN) used for weed
convolutional deep learning technique and SVM growth repugnance’s, with the classification of
classifier provide 96.88% accuracy. Zhang, cereal. In this research paper approx 18 weed
Wenhao, et al. [7] Has suggested a new image species are cover and 9649 images are used
architecture of RCNN for classification and for training for the computer system, the
detection of weed where weed leaf images were computer system can spontaneously, categorized
classified by PU learning technique, weed the weed into nine subgroups. That cans
characteristic extricates using positive negative performance using of this deep convolution neural
problem technique. The development of remark network (DCNN) which is estimate 2516 set of
the broadleaf was typical in the crop, VGGNet images, defluxion of two leaves having 96%
model useful for various broadleaf identification accuracy. Andres Milioto, and Philipp Lottes et
like amaranths Viridis, boerhavia diffua, anagallis al.[18] compartmentalization of the crop and
arvenisis,argemone Mexicana. Jalin Ya and Di weed in sugar beet plant using deep learning. The
Cicco, Maurilio et al. [8] The weed categorized stem of the sugar beet image implements a deep
accuracies were 70.99%, respectively manually convolution neural network (DCNN) scrupulously
weed detection and identification ware time detecting, the weeds with perception result
consuming, using Robot technology implemented achieve an average of 96.3%. T. Llorca et al. [19]
Tested is modern deep learning-based image identification of weed in tomato plant using
182
transfer learning technique using Google’s Pena, J .; Andujar, D.; Dorado, J .; Ribeiro, A .;
inception V3 model, which is used for image Lopez-Grandos, F et al. [32]the weed categorized
classifier provide the accuracy of 88.9%. Oktaviana accuracies were 70.99%, respectively Manually
Rena Indriani et al. [20] implement GLCM method weed detection and identification ware time
and Hue, Saturation, Value (HSV) calculations for consuming, using Robot technology implemented
image process that can calculate and determine Testbed is a modern deep learning-based image
the sophistication of tomatoes by using K Nearest segmentation technique that clearly differentiate
Neighbor (KNN), the researcher get complete monocot and die cot weed. Bakhshipour, A.;
testing after calculation efficiency rate is 100%, Jafari, A. et al. [33] Aichen Wanga,c, Wen Zhangb
GLCM’ s value is 9. James Perring et al. [21] et al. [34] examine 1119 plants 54 test 682
Write a survey paper to classify the annual weeds detection and detection accuracy is 0.37%.
according to 65 scientists from different fields like Therefore, the category approach insect the pixel-
ecology, taxonomists, etc. Aichen Wanga, c, Wen wise object base detection using deep learning
Zhangb, Xinhua Weia,c, et al. [22] prepare the VGG 16 FCN technique. Aichen Wanga,c, Wen
review paper which is helpful for researchers, they Zhangb et al. [35] demonstrated a framework to
are implementing using computer visualization classify weed images. Alek‘A r et al. [36] also use
with image processing for weed detection, also use pixel-wise image segmentation photograph
the deep conventional machine learning sequences allows our system to robustly estimate a
technique. This research paper also helps to pixel-sensible on weed, furnished comparisons to
prepare for Deerfield other today's tactics, and display that our device
Robotics.http://ecoursesonline.iasri.res.in/mod/p appreciably improves the accuracy of weed
age/view.php?id=101845. [23] This material is segmentation with retraining of the
weed classification in Weed Management in model.Parejo1’ Jin Su Jeong2,Julio Hernández-
Horticultural Crops which is completely helpful for Blanco1 et al. [37] implement an automated
the researcher. Lawrence, Wetzel, Arora, et al. approach for the detection of weeds like transfer
[24] Define aquatic weeds, and classification also learning technique reduce the time for
explains the ecological compact factor, Among 36 determining the weeds using pertained model
media of 12 aquatic weeds tested for growth of implemented on some weeds having better
eugenia, worm shows significantly luxuriant accuracy(90%).Heo choon Ngo et al. [38]
growth with Implication of Aquatic Weeds. implement weed detection using color
Manage, S.Abdolrashidi et al. [25] suggested that classification, using an automated image
two effective sets of abilities had been brought for classification system is designed using CNN which
use for iris recognition: scattering trade-based is distinguish between weeds and crops, also used
features and textual content centers. P rosti A. the robot Lego Mind storm EV3 which is directly
Ahmed S samai ,E bellin,D Russo et al. [26] connected to the computer will spray weed
recommended that clustering strategies determine directly into the area near or at which time the
the hobby of the lately introduced by.I, B.H .; weeds have been detected. Discussed by Compare
Zhang, J .; Zheng, W.S. et al. [27] weed detection, deep learning, 10 and 50 meters
Rakotomamonjy, A.; Petitjean, C.; Salaün, M.; implement on machine learning but the image
Thiberville, et al. [28] Discuss some techniques taken with different space. M. Ozoemena Ani,
for identifying and detect the weed for increase Ogbonnaya Onu, Gideon Okoro and Michael Uguru
the crop production using image processing. et al. [39] introduction of biological control
Reduce the weeds using automatic robot method on weeds. Paolo Bàrber et al. [40] get
technique with semantic segmentation CNN critical analysis of weed image identification, in
(mobile), feature extraction and recognition. this research paper approx 17000 of weeds images
Yang, X.; Huang, D.;Wang, Y et al. [29] briefly of the wheat crop, this data has been collected by
stated by this method exclusively classifies the which ATV-mounted camera, for weed detection
weed using UAV imaginary and transfer learning implement using fully convolution neural network
with FCN technology. Torres-Sánchez, J.;et al. (FCNN). Nima Teimouri, and Mads Dyrmann et al.
[30] drift the detection of broad leaf weed on [41] completely focused on weed growth and
various crops. In the in the weed classification implement a deep convolution neural network
process, an algorithm like multistage scattering (DCNN) used for weed growth repugnance’s, with
transformation was playing an important role, the classification of cereal. In this research paper
weed detection using conversion machine learning approx 18 weed image species are cover and 9649
technique and SVM classifier provide 96.88% images are used for training for the computer
accuracy. Peña, J.M. ; Torres-Sánchez, J.; system, the computer system can spontaneously,
Serrano-Pérez, A.; de Castro, et al. [31] López- categorized the weed into nine subgroups. That
Grandos, F.detection as stricken by quantitative cans performance using of this deep convolution
efficacy and sensor resolution. Fernandez- neural network (DCNN) which is estimate 2516 set
Quintanilla, C .; of images, defluxion of two leaves having 96%
accuracy. Andres Milioto, and Philipp Lottes et al.
[42] Compartmentalization of the crop and weed
183
in sugar beet plant using deep learning. The stem researcher. Lawrence, Wetzel, Arora et al. [48]
of the sugar beet image implements a deep Define aquatic weeds, and classification also
convolution neural network (DCNN) scrupulously explains the ecological compact factor, Among 36
detecting, the weeds with perception result media of 12 aquatic weeds tested for growth of
achieve an average of 96.3%. T. Llorca et al. [43] eugeniae, worm shows significantly luxuriant
identification of weed in tomato plant using growth with Implication of Aquatic Weeds. Nima
transfer learning technique using Google’s Teimouri, and Mads Dyrmann et al. [49]
inception V3 model, which is used for image completely focused on weed growth and
classifier provides the accuracy of 88.9%. implement a deep convolution neural network
Oktaviana Rena Indiana. et al. [44] implement (DCNN) used for weed growth repugnance’s, with a
GLCM method and Hue, Saturation, Value (HSV) classification of cereal. Andres Milioto, and Philipp
calculations for image process that can calculate Lottes et al. [50] compartmentalization of the
and determine the sophistication of tomatoes by crop and weed in sugar beet plant using deep
using K Nearest Neighbor (KNN), the researcher learning. The stem of the sugar beet image
get complete testing after calculation efficiency implements a deep convolution neural network
rate is 100%, GLCM’ s value is 9. James Perring et (DCNN) scrupulously detecting, the weeds with
al. [45] Write a survey paper to classify the perception result achieves an average of 96.3%.
annual weeds according to 65 scientists from
different fields like ecology, taxonomists, etc.
Aichen Wanga,c, Wen Zhangb, Xinhua Weia,c, et 3. Weeds classifications
al. [46] prepare the review paper which is helpful and Control Methods
for researchers, they are implementing using
computer visualization with image processing for
2, 50,000 plant species, weeds are approximately 250
weed detection, also use the deep conventional
species, primary in agricultural and non-agricultural
machine learning technique. This research paper structures. In recent studies, it has been found that the
also helps to prepare for Deerfield Robotics. above-described weeds strongly impact on agriculture
http://ecoursesonline.iasri.res.in/mod/page/view system which is the result of heavy loss in the agriculture
.php?id=101845. [47] This material is weed field. Therefore, it is required to identifying, controlling
classification in Weed Management in Horticultural and reducing their impact on the ecosystem
Crops which is completely helpful for the
Figure 1: Classification Based on Life-Cycle
3.1 Weeds classifications a) Annual: Annual weeds life cycle is one year.
This type of weeds like herbs with shallow roots
and stems are weak and propagate through seeds.
The weed is classified into two categories Annual seed after seeding die away and start the
based on life cycle, habitat, and morphology in production for the next generation of season.
figure 1 and based on ecology, soil type, and There are some most common annual weeds
septicity, and noxious weeds. (Table 1).
I. Based on Life span
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• Monsoon annual: This type of weed's life duration Vegetative and next year produce the seed and
is only four months. E.g Commelina benghalensis, flower. Biennials example: Alternanthera
Boerhavia erecta. echinata, Daucus carota.
c) Perennials: These types of weeds’ life cycle
• Winter annual: These weeds grew during winter
are more than two years. It has also been
sessions and propagate through seeds. Seeding dies categorized into three types.
away. lambs quarter, Chenopodium album e.g. Simple: Weed born by seed. Eg. Sonchus arvensis
lambs quarter Bulbous: Weed propagated from seeds. Eg. Allium
• Summer annual: Kharif corps. Foxtail. sp.
b) Biennial weeds: Biennial weeds life Corm: Plants breed through cream and seeds. Eg.
durations for two years. First-year they are simply Timothy (Phleum pretense)
Table 1.Example of weeds based on life cycle
Annual weeds Biennial weeds Perennials weeds
Monsoon Winter Annual Summer First Year Second Year Simple Bulbous Perennials Corm
Annual Annuals Perennials Perennials:
Commelina Lambs Quarter Kharif Daucus, Alternanthera Sonchus Allium Sp. Timothy
e.g. Annuals e.g. Arvensis (Phleum
Foxtail Pratense)
Benghalensis Chenopodium Ravi Carota, Echinata, Bermuda gras Hedge bindweed Japanese
Album Nulicauls knotweed
Boerhavia Lambs Quarter Kharif Biennials Daucus Carota Wild onion Yarrow Leafy spurge
Erecta Annuals Example:
II. Habitat weeds Immersed weeds:
This type of weeds completely grew up under the
Terrestrial weed: water and root in the mud. E.g. Nelumbium
That type of weed grew on land soil, called speciosum, Jussieua repens.
terrestrial plants. The examples of some
terrestrial plants are as follows: e.g Air potato Floating weeds:
Aquatic weeds: In this type of weeds, the leaves are gaggle and
Aquatic weed plants grew under the water and grew on the water floor both independently. A few
complete at least one or more years in a biological weeds are partially unfastened float and few
clock called aquatic weeds. It is also divided into mounted on mud, leaves upward push and fall
four subcategories like submerged, emerged, because the water level increments or diminishes
marginal and floating weeds (Table 2). expand at the water floor and not linked to the
dust base.
Submersed weeds: e.g echhornia , pistia, nymphaea e.g. Eichhornia
In general, weeds have grown under the water crassipes, Pistia stratiotes, Salvinia sp
and stems and leave underneath the water facial. Marginal weeds:
Example: Lemma, polyrrhiza e.g. Ceratophyllum This can develop in a wet seaboard with a
demersum.Ceratophyllum Australe Griseb, profundity. The root beneath the water and leaves
Ceratophyllum demersum L. (rigid hornwort or above the water. Anchored weed in water with
common hornwort) - cosmopolitan major foliage on the above surface. e.g Nilumbium
speciosum. Typha, Polygonum, Cephalanthus,
Scirpus, and so on.
Table 2. Example of aquatic weed.
Aquatic weeds
Submersed Weeds: Immersed Weeds Marginal Weeds: Floating Weeds:
Utricularia Speciosum Cephalanthus, Echhornia, Pistia ,Nympheea
Stellaris,Polyrrhiza Jussieu Repens. Scirpus e.gEichhornia crassps, Pistia stratotes,
Demersum. Salvinia sp., Nymphaea pubescens.
III. Classification according to the
cotyledonous character of
morphology:
185
animals or even as grazing the cattle devours this
The morphological plant of the plants categorized toxic plant life e.g. fastuosa (L.) Danert ,
on insignificance, and also it’s has categorized into Stramonium fastuosum (L.) are noxious to living
three types. things. The berries of Withania somnifera and seeds
a) Grasses: It is a Poaceae family, approx all weeds of Abrus precatorius are poisonous.
come under on this family called grass which has
spiral leaves. Parasitic weeds
Parasitic weeds are probably a mixture; the weeds
Sedges: depend entirely on the host plant, the parasites that
The weed is cyperaceous family graminoid, attack.
monocotyledonous flowering plant life known as Some parasites as given below:
sedges. Approx 5,500 species described but 2,000 Total root parasite – This type of plant depends
are identified. on another plant and gets nutrition from them.
Dendrophthoe,Orobanche ,Viscum,Santalum
Broad Leaved Weeds: Aeginetia, lathrea, cistanche etc.
This type of weed comes under on dicotyledonous
family, for example flavaria australacica, digera Partial root parasite – e.g Sandalwood tree,
arvensis, tridax procumbens.e.g. Rubus Spp., Witch weed, Rhinanthus.
Bramble, Butterfly-Weed etc Total stem parasite – e.g Dodder (Cucuta)
Cucuta rootless yellow color.
IV. Based on ecological affinities Partial stem parasite – e.g Viscum and
Loranthus.
Wetland weeds:
Aquatic weeds:
This type of weed are semi-aquatic, it can grow in
Aquatic weed plants develop in water and have a
two types of ecological condition first under the
life-cycle of at least of years and are classified into
dehydrated and moderately parched situation. The
four types such as submerged, emerged, marginal
dissemination of wetland by seed.
and floating weeds.
Irrigation Lands: The land weeds no longer require
greater water it will also no longer as dry land VII. Noxious Weeds:
weeds.
A poisonous or noxious weed plant discretionary
Dehydrate Lands: Dehydrate or drylands weeds characterized as being particularly unwanted,
are deep root system, dryland weeds adapt as inconvenient, and hard to control. The status of a
glutinous nature and hairiness plant as a poisonous weed will shift with the lawful
translation of a nation or a state, just as with the
V. Based on soil type (Edaphic) advancement of new weed control advances. The
toxic weeds have a huge ability to imitate and
Weeds of regur soil: scatter, and they embrace precarious approaches to
Those varieties of weeds are grown in the resist the man's endeavors to dispose of them. The
dehydrated situation poisonous weeds are some of the time additionally
Weeds of red clay soils: alluded to as exceptional weeds and offensive
It’ll consist of special kinds of numerous plants. weeds. Noxious weeds in India Cyperus rotundus,
Cynodon dactylon, Parthenium hysterophorus,
Weeds of loamy soils: Eichhornia crassipes, Solanum elaegnifolium, and
Those types of weed produce sewage like conditions Orobanche spp.
e.g. Leucas Aspera
VIII. Grassland Weeds:
Weeds of lateritic soils:
e.g. Lantana Camara, Spergula arvensis As the name shows, weeds having a place with this
class attack prairie, rangelands, and changeless
VI. Based on specificity: fields, which offer an unexpected biological
condition in comparison to the harvest lands. The
There are some weeds are identified by specificity, significant contrast between the two circumstances,
it has categorized into three types a). Poisonous from the viewpoint of perspective on weeds, is that
weeds, b). Parasitic weeds c). Aquatic weeds. while croplands are much of the time worked and
upset, the meadows stay undisturbed for an
Poisonous weeds extensive stretch. The meadow weed species, be
that as it may, must withstand visit munching, and
These cause livestock to the animals that are cutting, just as stomping on by the creatures. Some
accumulated along with barley and maintain to farm grassland weeds are equipped with mechanisms to
186
keep the animals away, like bitter leaves, poisonous
foliage, prickly shoots, and hard stems.
3.2 Weeds Prevention
Methods:
Farmers increase crop production if they remove
the weed farm crop, for this, they use a weed
removal technique, which is based on the
ecological theme. From crop management to
complete weed management. For example, weed
management with low nutrient management. -
External input system.
Figure 2.Weed control methods
These weed identification and control
techniques are classified into different categories
as given below in Figure 2.
I. Preventive Methods essential, herbicides can be a vital and powerful
a) Crop Rotation: The crop rotation is a aspect of herbicides.
traditional technique implemented by farmers for
increase the productivity and prevents the weed 4. Materials and Methodology
from the crop, simply means different crop grew
in the same field known as preventive weed
control. There are some weed control method
repeated yearly, given in table 3 This section laid-down the methodology and
II. Biological Control Method techniques of weed recognition and grouping as
The biological influences approach uses naturally
occurring enemies of the invasive plant to help
minimize its effects. Its objective is to Resume the Image segmentation and
weeds through its herbal opponents and attain classification
permanent weed management hose herbal images of
weeds from field, the weeds image acquisition this This is used to extract and classify based on image
parts the weed plant image continuously capture attributes. In the first process, the images are
images by the camera with high frame rate and captured by a digital camera stored. PNG, JPG,
resolution and data pre-processing with output JPEG, etc. The image acquisition involved three
images and feature extraction, detection for use the steps for pre-processing [20]. The first steps are
color analysis use is Hue, Saturation, Value (HSV) involved in the RGB image to grayscale images and
computation can be used for categorized to second, Steps include the resize image and finally
determine the mellowness level of weeds. Even filter the image [21]. Segmentation provides the
though inside the long run, organic management can solution to the image problem, each leaf has a
be cost-effective and diminish the prerequisite separate feature that significant information is
Control practices, now not all weeds are suitable completely helpful to the developer which is
for organic manipulation. recognized and classified. The GLCM is the
III. Cultural Control Method: methods used for texture analysis this degree is
The cultural method is commonly related to accomplished to give the characteristics or
farming systems, even though a few factors apply reputation of each photo on the way to be used for
to landscape and bush care practices. This may training and testing [22].GLCM is a group of
include the usage of plant species that overwhelm patterns that can be used to discover or classify
other plant means poisons. various capabilities of your application, with the
IV. Physical Control help of a recognition system (for example, an
Physically control is the elimination of the weeds ANN). First converts the RGB picture to HSV.
using physical or mechanical machines. The Later, it is important to scale the HSV matrix to
approach used often depends on the place of values between 0 and 64. It occurs that the co-
weeds to be controlled by a mechanical method, incidence matrix is computationally viable prefer
burning or with the aid of hand, etc.
V. Chemical control
The farmer can use the chemical to remove the
weeds but it will also affect to soil and crop,
although the usage of chemicals isn't continually
187
Table 3 Common crop rotation technique.
Cultural exercise Category Prevailing impact Instance
Soil Polarization Preventive Approach Weed threshold reduction Use Of Black
Irrigation and drainage Preventive Approach Reduction of weed Irrigation placement
system emergence
Cropashes Preventive Approach A discount of weed Suitable cultivation
emergence
Crop impartial association Cultural Method Development of crop Better seeding price with transparency.
rival, competence
Crop genotype desire Cultural Technique Development of crop Soil beret rates in elementary step.
competitive influence
Figure 3: Block diagram of weed classification and detection.
Next, that can compute the co-prevalence matrix photograph. The elements of this matrix rely on
for the H, S, and V matrices. Thus, you'll have the frequency of two detailed pixels. Both Pixel
three co-occurrence matrices, and it could be set pairs can vary relying on their community. These
parameters (entropy, variance) for each of these matrix elements consist of 2d-order statistical
matrices. It’s far essential to set up correlations [24].The implementation of segmented images
between the parameters, to determine which ones that can be transformed onto a gray level run
are applicable [23]. The GLCM houses of a photo length matrix. [25]. Flow chart of GLCM is given
are expressed as a matrix with the identical wide below in figure 4.
variety of rows and columns gray price in the
Start Gray scale Gray scale Matrix GLCM calculation End
afterRGB
pixel
Figure. 4 Flow chart of GLCM
4.2. Convolution Layers This review paper implements Transfer learning
the use with Convolution Neural Network (CNN)
188
for weed detection [13]. Convolutional neural used to become aware of the phototype from an
networks use to perform some operations on actual photograph. In this is research paper CNN
images and extract some useful information for apply to pick out the weed with the category. there
rained the model. The neural networks are a are many exclusive forms of photo category
collection of layers of neurons that are technique consist of a huge wide variety of facts set
interconnected and the outcome represents the like photograph net[11], The pre-trained networks
estimates. A Convolution neural network is which include the VGGNet [8], AlexNet [9],
different and contains three dimensions such as GoogLeNet [10], ResNet [11] [17]. Some other image
width, height, and depth [14]. data set provided by a digital camera set on (MAV)
Micro Arial Vehicle [1], Unmanned Arial Vehicle
a) Network Architecture: (UAV) [2], TAV mounted camera, which generates
the digital image of weeds, transfer learning use
The CNN includes three different layers such as with CNN for weed image classifier, some example
convolution, pooling, and classification. listed as in table 4.1
Conventional neural network (CNN) is comely assist
to deep studying network. CNN's are typically it's far
Table 4. Weed data execution using CNN
Serial no Steps Explanation
1 Data acquisition Weed plant image acquisition by the camera with a high frame rate and
resolution
2. Images Pre-Processing and techniques Feature extraction, detection for use of the GLCM, and HSV method provide
high evaluation correlation and homogeneity of pictures.
3. Classifiers Conventional neural network (CNN), Pooling, flattering, transfer learning to
use with CNN for weed image classifier
4. Classified Weed It can classify die cot and monocot and broadleaf crop weed, for example,
Cyprus, Amaranth
Hue Saturation Value (HSV) color has 3 elements, human beginners. Transfer learning is ordinarily
known as Hue (H), Saturation (S), and cost (V) utilized in computer imaginative and prescient and
[19]. The HSV consists of 3 elements, wherein Hue herbal language processing obligations like
represents coloration, dyeing for saturation sentiment evaluation due to the huge amount of
brightness and fee degrees, dominance and computational power required [37].
brightness degrees. [20]. The second segment of
weed detection recalls the classifiers, the weed 4.3. Transfer Learning Approach
diction is two-step trouble i.e. weed and grain
plant. [27].Another classification method also a) Training to reuse
includes K-nearest neighbor (KNN), Complex Tree,
and Logistic Regression [36]. There are transfer b) Using a Pre-Trained Model
methods for the switch of knowledge among c) Feature Extraction
Figure. 5 Transfer learning for weed detection
Transfer learning isn't sincerely a device gaining instance, active getting to know. It’s also now not
knowledge of approach but may be visible as a a one-of-a-kind part or takes a look at-place of
"layout technique" within the subject, as an device mastering [38]
189
5. Comparative Analysis
Article name Problem description Deep learning Deep Learning Overall Accuracy Reference
architecture model. no
Object Demonstrate that our proposed Faster RCNN, ResNet101 Detector performance [7]
detection using PU type loss outperforms the PU learning Faster R-CNN Accuracy is 88.9%
faster RCNN same old PN loss on PASCAL Picks due to the using both PN
and PU VOC and MS COCO throughout fact filters inner
Learning a number label missing, as well as that acts as
(positive on visible Genome and Deep feature Detector.
unlabeled)missi Lesion with complete labels.
ng data image.
(11 Feb. 2020)
Weed Computerized identification and Feedforward V-Net, mobile- An effective system for [3]
classification in selective spraying of weeds (such Neural network nets, DenseNet detection and
grasslands as dock) in the grass can provide for leaf sickness and ResNet Classification
using very considerable long-time detection architecture Detect the weed 47 -67%
convolutional period ecological and cost CNN(mobile)
neural advantages. Although the device
networks. (sept. imaginative and prescient (with
2019) the interface to appropriate
automation) affords a powerful
means of achieving this, the
associated demanding situations
are bold, because of the
complexity of the pics.
Localization Any weed Localization And Use DCNN for Classification The superiority of the [5]
And classification of weed using a Weed based on SVM scatter set of rules with a
classification of scattering transformation classification Classifier, V-Net weed detection Accuracy
weed using a Detection of weeds inside the also implantable architecture. of around ninety-five%
scattering direction of extreme density performance on a different single scale
transformation existence plant life from the and multi-scale strategies.
(26 Jan 2019) pinnacle view in-depth snapshots.
An annotated artificial statistic-set
was positioned underneath the
size of an employer and a
simulator is proposed for a
reproducible technique.
Broadleaf weed Detection of extensive grass Deep learning ResNet101 using Those fashions carried [7]
using DL CNN weeds in turf grass the use of CNN (DL‐CNN) both VGGNet-16 out excessive F1 rankings
and data collect VGGNet became an exquisite models and and Google net, (> 0.99) and ordinary
using model for detecting various broad Faster R-CNN ResNet, accuracy (> 0.99) with
VGGNET (22 floor weeds that develop in DetectNet. recall values of 1.00
Jan 2019) Bermuda grass and the detection inside the test dataset.
of cutleaf knight-primrose
(Panthera laciniate Hill) in Bahia
grass. DatetechNet changed to a
high-quality version to be carried
out. The mastering fee coverage
exponentially decays.
Weed Use image data taken from the DNN, an Signet, MAV Accuracy of crop = 68%, [2]
segmentation unmanned aerial car (UAV) for encoding part (Macri Arial weed = 57%
and mapping the weed and crop with a with VGG16 Vehicle Under
classification deep neural network (DNN). layers. DNN, an the Curve (AUC)
using Deep encoding part with VGGNet-16
neural network with VGG16
Image taken by layers
UAV(Unmanne
d Arial Vehicle)
(7 Sep 2018)
Use FCN (Fully Weed And Crop Identification on Transfer learning Classifier, patch, The accuracy of the FCN [4]
Conovional rice field transfer learning and for Patch base CNN and pixel base method is 0.935 and
Nural Network) image use UAV(Unmanned Arial And pixel base CNN and weed recognition was
190
transfer Vehicle) FCN(Fully 0.883.
learning and for convolution
image use network),
UAV(unmanne Bayesian
d Arial vehicle)
(26 April
2018)
A study on Identification of Broadleaf weeds Cascaded CNN SegNet, MAV Achieve _ 0.8 F1-score [6]
Image-based analysis and system and SegNet, (Macri Arial and get 0.78 regions
Broadleaf implementation based on SVM. MAV (Micro Vehicle Under underneath the curve
identification The critical component within Arial Vehicle) the Curve (AUC) (AUC) class metrics
weeds analysis smart sprayers image-based weed
and system detection.
implementation
based on SVM,
and machine
learning (2018)
'WeedNet: Selective weeding measures are a Cascaded CNN SegNet, MAV Achieve _ 0.8 F1-score [1]
implemented to critical step in self-sufficient crop and SegNet, (Macro Arial and get 0.78 regions
era sizeable control associated with crop MAV (Micro Vehicle Under underneath the curve
Semantic video health and yield. but, an important Arial Vehicle) the Curve (AUC) (AUC) class metrics.
type the usage venture is to discover dependable
of multispectral and correct weeds to limit harm to
photo and the encompassing plants. In this
MAV for clever paper, we gift a method for dense
farming '(11 semantic weed sorts with
September multispectral pix amisped through
2017) a micro aerial automobile (MAV).
Using KNN and In tomatoes leaf has one-of-a-kind K-NN, GLCM, GLCM and HSV Use color space [11]
GLCM, HSV maturity level; consequently, it's and HSV color space techniques like GLCM
color space far necessary to apprehend the technique and HSV color space
techniques in proper Sample to decide the technique accuracy rate
the tomato extent of maturity. Texture 100%.
plant. evaluation may be processed with
the use of the grey level Co-
incidence Matrix (GLCM)
technique.
Using This analysis more increased Weed Weed Using DCNN got 94% [13]
machine vision weed detection employing a discrimination identification and accuracy.
and image ground-primarily based mostly using CNN discrimination
process system ingenious and anchoring from the crop
techniques: and image processing techniques plant
Weed
discrimination
Weed Crop weed image mapping using CNN and Keras(Tensor 91% [14]
Classification water shade method with different SegNet, MAV Flow FW )
species. (Micro Arial
Vehicle
Weed Weed Maps, using thresh holding CNN and VGG-16, 90.08% [15]
Classification method for weed control in early SegNet, UAV DenseNet- (DenseNet)
climates, inflicting star reflections (Unmanned Arial
and troubles. Vehicle
Weed Crop and weed classification in CNN SVM: 98% (CAFFE FW) [16]
Classification the soybean plant EfficientNet 98%
In-depth study In most cases, weed management Deep VGG-16, 90.08% (DenseNet) [17]
for weed within the traditional method convolutional DenseNet
detection: Deep depends on manual labor. This neural networks
sensory neural method takes time, contributes to (DCNN)
network high costs, and vital yield losses.
architecture for The standard application of
the plant chemical weed management,
classification however, goes against the hassle
of sustainability. To handle this
use of laptop computer imaginers
and anchors, preciseness
agricultural researchers have used
remote sensing Weed Maps,
191
however, this has become mostly
useless for weed control in early
climates, inflicting star reflections
and troubles. Satellite imagination
includes cloud cowls.
Crop and weed Weed control pest on rabi crop R-CNN machine DNN has SegNet 96.08%(SegNet) [19]
image mapping session weeds using image learning Deep architecture, an
using machine segmentation method for dense learning CNN coding dispense
learning UAV semantic weed sorts with (DL‐CNN) with VGG16
remote sensing multispectral pix through a micro models and
aerial automobile (MAV). Faster R-CNN
Machine
learning,
Using Deep This looks at situ images Deep learning Our DNN has 78% Maximum accuracy [8]
CNN find the involving 18 weed species grown CNN (DL‐CNN) SegNet
Weed growth within a Time,8000 leaves of models and architecture an
competition these drawings were used for the Faster R-CNN encoding part
from the crop. trained of the weed statistics is Machine with VGG16
taken from the rabi crop. learning, layers.
Growth of Competition of weed in flower Statically Different coffee Convolutional neural [10]
weeds in young plants using ResNet-50 in CNN analysis of data plants using network using
coffee plants architectures study technique of taken from the ResNet-50 inceptionv3 model on
using CNN. interference between flowers. field 2000 pictures tested,
which also are several in
cropping.
Multiclass Soil types, photograph judgments, Deep Learning CNN architecture ResNet-50Validity
weed Species and lighting fixtures situations. CNN (DL-CNN) Inception-V3 accuracy of 96.7% and
data set Dataset The common ordinary model and 97.6%.
for Deep performance of this method met quicker R-CNN
learning: Deep the maximum accuracy of 90.79% machine
Weeds learning,
Weed Survey paper measure completely Survey paper on Mini tab on vex Researchers for weed [12]
classification: different views equivalent to Weed platform weed detection inside the
using Image implications for regulation of classifications science. discipline has been
Net weeds, terrestrial weeds, and discussed
annual weed
Weed The weed management practices Deep VGG-16, In 425 French farm [18]
Management in used by farmers in conservation Convolutional DenseNet- with (Dense Net) 96.09%
the transition to agriculture and the modifications Neural Networks
Conservation initiated thru its adoption. (DCNN)
Agriculture: a
Review
Applications of Real-time selection support Deep learning ResNet101 using (CAFFE FW) [20]
Computer gadgets can beautify crop or plant CNN (DL‐CNN) both VGG-16 98%
Vision in Plant boom, consequently, increase models and and GoogleNet,
Pathology: their productivity, best and Faster R-CNN ResNet,
financial value. It also permits the DetectNet
North American nation to serve
the character by watching plant
growth in equalization the
surroundings. pc inventive and
presenter technology has valid to
play a vital place among the
degree of programs equivalent to
medicine, defense, agriculture,
remote sensing, enterprise
analysis, etc.
explores different techniques that are used for
weed management such as artificial intelligence,
6. Conclusions machine learning, and deep learning with their
pros and cons. This paper explains different steps
This paper explains different categories of weeds to detect and analyze weed-based images. The
and control methods used in crops. This paper also steps are pre-processing, Classification,
192
identification of crop weed and cereal and Glynn Wright. "Broad-leaf weed detection
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intelligence, and deep Conference on Image, Vision and Computing
Learning processes techniques. In this paper, the
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methodologies are mentioned. The emerging [8]. Di Cicco, Maurilio, Ciro Potena, Giorgio
approach CNN with transfer learning ideas can be Grisetti, and Alberto Pretto. "Automatic
included right into a speaking device that could model-based dataset generation for fast and
similarly help farmers in identifying crop weeds of accurate crop and weeds detection." In 2017
plants. The precise category model allows in IEEE/RSJ International Conference on
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indices for identifying and monitoring winter
wheat diseases.’ Appl. Earth Obs. Remote
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