RETRACTED: Advanced techniques for Weed and crop identification for site specific Weed management

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Review

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Advanced techniques for Weed and crop identification for site specific Weed management Karan Singh, K.N. Agrawal, Ganesh C. Bora*

Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Blvd, Fargo, ND 58102, USA

article info

Weed management plays a major role in the production and economic benefits derived by agricultural industry worldwide. The monitoring of weed pressure, economic threshold,

Received 26 July 2010

yield loss and environmental impact is critical for sustainable agriculture. Currently

Received in revised form

research is being carried out relating to weed mapping at field scale and the development

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Article history:

of machine vision controlled equipment. Remote sensing and aerial imaging techniques

29 December 2010

utilised for site-specific weed management have limitations due to the accuracy of satellite

Accepted 9 February 2011 Published online 17 March 2011

imagery, its cost and timing. The advent of optoelectronic sensing and enhanced computing has provided a demand for the development of the real-time assessment and management of weeds in fields. The available technologies that can be used for developing a ground-sensor based system to assist in determination of weed pressure and economise on the application of herbicide under field conditions are reviewed. These technologies

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include image-based identification and spectroscopic methods for weed identification and threshold determination. The various methods studied and the concepts pursued by

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various researchers are discussed in the paper.

Introduction

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Humans have long recognised that weeds compete with desirable plants for water, sunlight, nutrients, and space; thereby reducing their productive capacity. Management of weeds is an essential component of any cropping system and chemical application is one of the most important methods. For example, herbicides were applied up to 99% of soybean crop in the twenty states of the USA during 2002 (USDA 2003a; 2003b). Site-specific weed management (SSWM) has been practiced since the beginning of the selective culture of specific plants for human or animal consumption. In its most fundamental form, man has used SSWM to visually detect unwanted plants and use this information to remove them manually. This approach has remained common with time and is still practiced in the less developed parts of the world. However,

Published by Elsevier Ltd on behalf of IAgrE.

with the advent of mechanised agriculture and chemical herbicides, the approach shifted towards weed management on a whole-field basis, e.g. application of herbicides on the total cultivated area. This approach led to increased agricultural productivity, the ability to manage larger acreages effectively, and substantially reduced labour costs when compared to manual weeding. With the emergence of rapid computing coupled with optoelectronic systems came the development of methods for applying herbicides only where needed. Typically, this method used a non-selective herbicide and care was needed to minimise damage to crop plants. Traditional approaches to herbicide application are based on an assumption that weeds are distributed uniformly in fields. However, numerous studies have shown that weeds are not uniformly distributed across a field; they tend to be clumped together in patches (Cousens & Woolcock, 1997; Dieleman, Mortensen,

* Corresponding author. Tel.: þ1 701 231 7271; fax: þ1 701 231 1008. E-mail address: [email protected] (G.C. Bora). 1537-5110/$ e see front matter Published by Elsevier Ltd on behalf of IAgrE. doi:10.1016/j.biosystemseng.2011.02.002

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only applied to areas where weeds were detected. Patch spraying relies on the accurate mapping of weeds at a spatial resolution finer than that of current spray equipment (Brown, Bennett, Groudy, & Tardiff, 2000, p. 12), and researchers have demonstrated that relative to broadcast applications significant savings can be achieved using metre and sub-metre accuracy maps (Medlin, Shaw, Gerard, & LaMastus, 2000; Rew et al., 1996; Stafford & Miller, 1996). Weed maps used in patch spraying have been constructed primarily from data collected through visual scouting whilst on foot or in a vehicle (Goudy, Tardif, Brown, & Bennett, 1999; Lamb & Brown, 2001; Rew et al., 1996; Stafford, Le Bars, & Ambler, 1996). Johnson, Mortensen, and Martin (1995) constructed spatial maps using the economic threshold of the weeds rather than detection based on extensive scouting data from maize and soybean fields. They reported that chemical requirements could be reduced to a large extent if weed maps were used in combination with a herbicide application system. Gerhards, Sokefeld, Schulze-Lohne, Mortensen, and Kuhbauch (1997) observed that herbicide use could be reduced up to 50% by intermittent spraying using an economic treatment threshold in winter wheat fields. In an experiment carried out by JuradoExposito, Lopez-Granodos, Garcia-Ferrer, and Atenciano (2003) using an economic threshold in sunflower cultivation reductions in herbicide use at two locations ranged from 61% to 1%. A simple way of establishing economic treatment thresholds is to start with the cost of treatment and expected economical benefits accrued with the treatment. For example if one wishes to estimate an economic treatment threshold for a pest management treatment, then the percentage crop loss necessary for treatment to be economically viable is required. This can be estimated from the expected yield of the crop, the price of the crop at market, the cost of the treatment and its effectiveness. Treatments below the economic threshold, where their expected losses are less than the cost of the treatment are, of course, not advisable. The economic treatment threshold is often estimated as percentage crop loss or yield loss due to not applying the treatment. This crop loss is directly proportional to cost of treatment and is inversely proportional to expected yield and expected price of the produce. The effectiveness of the treatment is also inversely affects the percentage crop loss. Some researchers have used weed count thresholds for site-specific weed management. In one such study, Goudy, Bennett, Brown, and Tardif (2001) observed in a cornsoybean rotation that site-specific weed management, using a treatment threshold of >1 weed shoot m2, reduced the area sprayed by as much as 26%. Luschei et al. (2001) conducted onfarm tests to validate gains from using site-specific weed management compared with a conventional system in dryland spring wheat production. Their results showed that yield was unaffected by treatment strategy; however, higher net returns were realised at two of the four study sites because of the reduction in area treated. Only one weed species was scouted, and the treatment threshold was based on presence or absence of this weed. Once the zoning information is completed, prescription maps are developed for field application. Prescription maps are the final part of the weed management process. These are exciting part as the feeling we get apply the right rate in the

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Buhler, & Ferguson, 2000; Gonzalez-Andujar & Saavedra, 2003; Mortensen, Dieleman, & Johnson, 1997). Several studies have investigated using site-specific weed management to take advantage of the inherent patchiness of weed populations within agricultural fields. The hypothesis was that spot or patch spraying may prove to be effective and could reduce the amount of herbicide applied. Much research has also been conducted to develop techniques on weed detection and spray control. To date most of these systems have been tested for wider row spaced crops like maize. For weed detection in small-grain crops, such as wheat, having narrower row spacing, research work appears yet to find a solution. Remote sensing provides a non-invasive method of acquiring a synoptic view of weed populations on ground targets (Goel et al., 2003; Lamb & Brown, 2001). This technique has been proved successful in detecting spatial variability of many pasture and rangeland weeds. However, satellite-based remote sensing is, in general, limited as a tool for real-time and in-field weed monitoring because of its insufficient spatial resolution. Moreover, this technique is not likely to be adopted extensively due to its high cost. Targeting individual weed species or weed patches with higher efficacy is possible using the techniques of site-specific weed management. Literature suggests that site-specific weed management concentrates two approaches, establishing either an economic or weed density threshold. The objectives of this study are to (1) assess the state of art in site-specific herbicide application and (2) to ascertain the possible interventions, which can be used to benefit from site-specific weed management.

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2. Weed mapping and site-specific herbicide application

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The spatial distribution of weeds is non-uniform (Brown, Steckler, & Anderson, 1994), and therefore it is possible to reduce herbicide application to only areas where weeds are located (Lindquist, Dieleman, & Mortensen, 1998). Using site specific application it is estimated that more than 40% of herbicides could be saved (Brown & Steckler, 1995). Moreover, the different weed species within a field do not have the same effect on the crop, especially with respect to competition for water and nutrients. Studying their competitiveness should help to determine optimal weed management (Assemat, Champroux, & Ney, 1995). Also, weed competition is important in the early stages of growth of the crop. Consequently, in order to eliminate weeds as soon as possible it is important to predict and monitor the growth of plants. In order to manage weeds within a field, three essential sources of information are required: (1) Location and threshold; to allow accurate weeding; (2) Species identification; to study the interactions between different weed populations and to select the best chemical product; and (3) Growth stage; to study the competitiveness of each species and to determine the best time of treatment.

Rew, Cussans, Mugglestone, and Miller (1996) reported saving of 34e97% chemical were possible if herbicides were

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2.1.

Remote sensing and aerial imaging

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Weed Detection

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The detection and mapping of weeds with remote sensing technologies requires that differences in spectral reflectance exist between weeds and their environment and that the spatial and spectral resolution of remote sensing equipment is sufficient to detect these differences (Lamb & Brown, 2001). The resolution necessary to construct weed and herbicide maps in crops may vary among management systems. For example, the criterion for applying herbicides based on remotely sensed data might simply be the presence or absence of weeds. Such an approach can reduce herbicide use substantially (Vrindts, De Baerdemaeker, & Ramon, 2002). Remote sensing in wide area of coverage has most often been associated with spectral sensors mounted on fixed-wing aircraft or satellites. Multi-spectral sensors use a few (usually three to five) spectral bands those are 10e50 nm wide. Aerial

Image based

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Spectroscopic based

Wide range sensors

Web cam, CCD Camera

Remote sensing, aerial imagery

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Close range sensors

•Based on • Shape • Size • Colour • Texture

Based on • GPS • GIS • Profile maps

Methodology • Threshold values • Discriminant analysis • Vegetation indices • Classification algorithms

(i.e. aircraft-based) systems usually have a spatial resolution of 0.5e4 m (each pixel represents this area in one dimension) but satellite systems provide multi-spectral imaging spatial resolution of 3e40 m. Both provide the advantage of regular coverage of an area in either a directed (i.e. aerial) or autonomous (i.e. satellite) approach with broad area coverage each time. These systems provide the post data processing and weed detection to herbicide application maps. This provides a distinct advantage over ground-based systems, because real-time sensing and processing requirements are greatly reduced and the applicator is simply required to have suitable hardware and software that can control the herbicide application. However, with the existing spatial resolution of these systems, they have not been effectively developed for weed. Weed patches are detected by their brighter pink colour. Weed detection and control is usually emphasised more during the earlier stages of crop production because excessive weed growth affects yield potential. At early stages weeds and crop both are often only a few mm in diameter, thus requiring high populations in substantial patch sizes for detection to occur. The sensors mounted on the satellite platforms are typically optimised to collect a few spectral bands, creating images that are useful for a broad range of purposes, but are particularly suitable for differentiating green vegetation from soil, water, and man-made features. As such, these bands are not optimal for detecting the subtle spectral differences between plants of different species. In addition, other key limitations of the technology include the potential for cloud cover and infrequent revisit rates that make them unsuitable for effective decision-making during the period of critical crop growth management. Aircraft-based sensor systems can often overcome many of these problems, and further research continues in applications development for these systems. Aerial imaging has been effectively used for weed detection in two ways. Firstly, detection of invasive species in rangeland and natural areas has been shown for a number of species. Detecting these species does not require early recognition and control, and the spatial resolutions of these systems are sufficient for detecting patches that are usually expected. Key phenological differences (e.g. flower or leaf colour) can often be determined at different points in the season, making it much easier to use the existing spectral bands to detect a given species. Further details of the application of remote sensing can be found at Rew, Maxwell, and Aspinall (2005) and Lass, Prather, and Glenn (2005). Lan, Huang, Martin, & Hoffmann, 2009 integrated a multispectral camera with a specifically designed camera control system for airborne remote sensing in pest management. The automated multi-spectral imaging system was able to consistently produce images at or near vertical nadir view for the spatial, spectral, and temporal analysis of pest issues. Example imagery showed the utility of the flight dynamics control system in reducing spatial errors caused by airplane roll, tilt, and yaw. A number of weed species, particularly perennials, are most troublesome during harvest, and are also most effectively controlled late in the season (Shaw & Mack, 1991). Thus, detection may be easier (simply detecting green vegetation indicates weed presence). Menges, Nixon, and Richardson (1985) used remote sensing to detect weeds within the crop, using differences in spectral reflectance at

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right place on the farm. Site-specific weed management utilises spatial information about weed distribution to apply control tactics only where weed density is economically high within a field. Prescription maps for site-specific weed management can be generated based on a realistic range of economic thresholds. Basically there are two techniques employed for weed detection or weed mapping. The methods and techniques employed for weed detection are summarised in Fig. 1. The image-based method employs camera and the spectroscopic weed detection method employs wavelength reflectance and peak differences charts.

Visible, infrared, fluorescence, and multispectral bands

Based on • • •

Wavelength reflectance charts Peak differences in the charts GIS and GPS

Methodology • Discriminant analysis • Vegetation indices • Classification algorithms

Fig. 1 e Commonly employed methods for crop-weed detection.

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(2006) have tested the abilities of four line detection algorithms (stripe analysis, blob analysis, linear regression and Hough transform) to determine the position and the angle of the camera with respect to a set of artificial rows with and without simulated weeds under controlled light conditions. Under their special laboratory conditions where the crop rows were simulated by black line patterns and the weeds were simulated by pieces of black form placed on a 3D-moving table, they have concluded that the stripe analysis algorithm can be considered the best, based on its insensitivity to noise and fast processing.

Image based crop-weed discrimination

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2.2.

Image based weed detection system classify plants on the basis of the colour, shape, or textural features of the weeds (Chen, Chao, & Kim, 2002; Cho, Lee, & Jeong, 2002). Different morphological features were extracted from digitised images of plants and these features were compressed and used for identification models. In this system sensor is mounted on a tractor or spray applicator, weeds are detected, the data processed, and spray decisions are made in the field. This provides a number of advantages over aerial imaging. In particular, spatial resolution can be several orders of magnitude higher than with aerial systems. It is a standard technique, which allows an easy detection of simple predefined shapes (lines, circles, ellipses) in images. Hough transform (Hough, 1962) has been extensively used in many agricultural applications due to its usefulness and robustness. For example, in forestry industry the circular Hough transform was proposed to determine from simulated and real images the diameter distribution within a stack of logs (round wood timbers) under both laboratory and field conditions. In some cases, good classification results are obtained. They may be attributed to particular conditions, such as crop regularly spaced in the field with no overlapping (Borregaard, Nielsen, Norgaard, & Have, 2000; Feyaerts & van Gool, 2001), clearly different macrostructures of weeds and crops (Franz, Gebhardt, & Unklesbay, 1991a; 1999b) or presence of a particular colour on certain plant stems (El-Faki, Zhang, & Peterson, 2000; Zhang and Chaisattapagon, 1995). Figure 2 presents the major steps in image processing for weed discrimination. The basic methodology consists of acquiring the image and pre-processing the images to improve qualities using filters. Colour images obtained were converted in to grey level images and later binary images were created for easier identification of weed. Image segmentation was initially focused to detect weed seedlings based on geometrical measurements such as shape factor, aspect ratio, and length/area (Pe´rez, Lo´pez, Benlloch, & Christensen, 2000). Guyer, Miles, Gaultney, and Schreiber (1993) used 17 features to delineate various weed plant species. In traditional plant taxonomies plants are identified based on shape features, colour, texture, etc. Although there are methods to identify individual shapes, the major challenge is to separate one green leaf from another green leaf. It becomes more difficult when two different shaped leaves overlap. Problem also arises when individual leaves have similar boundary characteristics and it becomes difficult to define and perform subsequent shape analysis. Various studies

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different wavelengths. Brown et al. (1994) used aerial images with four spectral filters to discriminate seven weed species. It is then used to build prescription maps for weed control within the field (Brown & Steckler, 1995). Limitations of remote sensing information and optimal parameters for remote sensing data have been suggested by several authors (Moran, Inoue, & Barnes, 1997; Robert, 1997; Thenkabail, Smith, & Pauw, 2000). Zwiggelaar (1998) mentioned that spectral properties are not sufficient to provide a robust discrimination between species. They should be complemented by other sources of available information. Reoccurring topics have been spatial resolution, spectral resolution, temporal frequency, and processing time for the images. Current technology and recent advances have overcome some of the limitations. A review of current and future satellite-based imagery and their respective capabilities has been provided by Moran et al. (1997). The degree of spatial resolution required is dependent on the task of interest (Moran et al., 1997). The optimal spatial resolution that has been recommended for agricultural applications such as crop monitoring is 2e4 m (Moran, 2000). A pixel size of 2 m would allow for management units on the order of 10 m (Moran, 2000). Other applications, such as early-season crop growth assessment, weed and insect monitoring and fertiliser application monitoring would require finer resolution (<2 m), dependent on the size of the mapping unit or detection of edges of certain anomalies that is required. At present, multispectral imagery with 4 m spatial resolution has been achieved with satellites. Aircraft-mounted sensors can provide finer spatial resolution by varying the flight altitude and provide the convenience of capturing images on demand (Lamb, Weedon, & Rew, 1999; Rew, Whelan, & McBratney, 2001). Rapid advancement in remote sensing technology poses another challenge for equipment and software changes in a short span of time and it has been a limitation for the use of remote sensing techniques in agriculture (Johannsen, Carter, Morris, Ross, & Erickson, 2000). To quantify weed infestations from an image, detection accurate crop/weed discrimination is required. In the case of agronomic images acquired in shot with a large field of view, it is necessary, firstly, to identify crop rows from a line detection algorithm and secondly to discriminate between crop and weeds by a segmentation algorithm. In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyse. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. Segmentation algorithm may be used to separate out different type of species based on the image related parameters. Among the main line detection algorithms, Bobillet et al. (2003) have presented a method allowing an accurate detection of a large number of vine rows in high resolution remote sensing images. For vine row detection, a network of deformable templates (i.e. snakes) has been successfully applied despite a prohibitive processing time. Fontaine and Crowe

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or weed and can be utilised for differentiating between two species of the crops or crop and weed. These features could also be utilised for further analysis.

Image acquisition

Pre-processing of the acquired image

2.3.

Region based segmentation

Feature extraction

Decision making/ weed detection Fig. 2 e Major steps in image processing and weed identification.

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conducted on image-based identification of weed and various classification features used are listed in Table 1. Colour images were successfully used to detect weeds and other types of pests (Søgaard & Olsen, 2003). Yang, Prasher, Landry, and Ramaswamy (2003) estimated weed coverage and weed patchiness based on digital images, using a fuzzy algorithm for planning site-specific application of herbicides. Recently, Gerhards and Oebel (2006) used real-time differential images obtained with a set of three digital bispectral cameras to detect small weed seedlings in different crops. Other approaches have used colour indices to distinguish plant material from the background (Ribeiro, Ferna´ndezQuintanilla, Barroso, & Garcı´a-Alegre, 2005; Thorp & Tian, 2004). Bacher (2001) estimated weed density in a field of spring barley by image binarisation and morphology followed by the identification of crop rows using information on distances between rows within the crop to decide on spraying. This process served to make weed plants appear isolated from the crop. Computationally this is still a very difficult challenge given that there is usually less than 2 s between weed detection and herbicide treatment unless the applicator is moving very slowly. In-depth reviews of these technologies can be found at Brown and Noble (2005) and Thorp and Tian (2004). The literature has revealed that researchers have followed many different ways for weed detection and decision-making but the most common steps include image acquisition. After acquiring an image, it is processed for band separation, or an excess green image is generated and removing blobs, holes, shades, etc from the image. The processed image is then converted in to binary image using threshold. Various methods of such thresholding have been utilised by different researchers. Based upon threshold values regions of similar values is segmented and each segment is treated as a region of interest (ROI). Once the ROI is defined, various geometrical measurements are obtained such as height, length, minimum bounding rectangle, major and minor axis, perimeter, area, textural parameter such as energy, entropy, contrast, moments, etc. These features can be distinctive to a particular species of crop

Multispectral imaging involves making images using more than one spectral component of energy from the same region of an object and at the same scale. Optical sensors have found increasingly more applications in such systems. Most optical sensors detect weeds based on plant spectral characteristics (Felton & McCloy, 1992; Vrindts & De Baerdemaeker, 1997; Wartenberg & Schmidt, 1999). The advantages of this type of system are low cost, less-complex system configuration, and greater processing speed. Each channel of a multi-spectral image may be displayed as a greyscale representation or in combinations of two or three channels as a colour composite image. Multispectral imaging has improved the capability of handling object characteristics beyond visible spectrum to NIR, IR and Thermal Infra-red regions. This is in addition to the texture, geometry, and context that would normally be expected from images. Spectral resolution refers to the amount of the electromagnetic spectrum that is detected and is usually given as a range of wavelengths. The term “band” refers to the width and specific location within the electromagnetic spectrum. To differentiate between weed, crop and soil, numerous researches have been conducted to analyse the factors that influence spectral reflectance of leaves. Several authors investigated multi-spectral imaging systems to differentiate weeds from crops. Within the scope of recognising weeds from crops, numerous researches have been conducted to analyse the vegetation indices that are computed from spectral reflectance of leaves. This parameter is influenced by the interaction between the species and the growth stage (El-Faki et al., 2000; Franz et al., 1991a; 1991b; Zhao, Li, & Qi, 2005), by stresses such as water or nitrogen deficiency (Goel et al., 2003; Zhao et al., 2005), by the angle between the leaf and the camera optical axis (Franz et al., 1991a; 1999b; Haralson et al., 1997 cited in Noble et al., 2002) and by the substrate nature (Noble et al., 2002; Noble and Crowe, 2001). Senescence also alters the spectral characteristics of plants (Daughtry & Biehl, 1985). Two commercial products, WeedSeeker (Patchen, Inc., Los Gatos, CA, USA, 2001) and Detectspray (North American Pty, Ltd., NSW, Australia, 1995), reached a weed-detection rate of 95% (Blackshaw, Molnar, Chevalier, & Lindwall, 1998). Spectral imaging techniques that combine spectral and spatial information have also been studied for their potential in weed detection (Alchanatis, Ridel, Hetzroni, & Yaroslavsky, 2005; Feyaerts & van Gool, 2001). A research group at the Kansas State University, Manhattan, Kansas, USA studied spectral characteristics of stems and leaves of five crops and 30 weed species and selected feature wavelengths to establish effective, illumination-insensitive colour indices during 2002e03 (Wang, Zhang, Wei, Stoll, & Peterson, 2007). Various vegetative indices used for spectroscopic studies for weed detection is given in Table 2. Some of the studies were conducted to select the most appropriate wavelengths for discrimination of crop and weed. Two methods are commonly reported in literature: spectrophotometric and camera/filter methods, used either in

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Image binarization

Multi-spectral imaging

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Table 1 e Few studies on vegetation detection using imaging techniques. Parameters Considered Shape

Soybean and weed

Area, perimeter, eccentricity, circularity Area, eccentricity, convexity, roundness e

Excess green

Area, perimeter, eccentricity, circularity e e

Excess green

Homogeneity and differences in crop and weed growth Colour images and GPS based map

Carrot and weed

Crop and weed height

Crop and weed Blueberry and weed

Hue, saturation, intensity Excess green

Texture

Radial spectral energy e

Bayesian classifier

Mathanker et al. 2007

Fuzzy logic

Hemming and Rath, 2001

Blob colouring analysis Green colour percentage levels Green colour

e

Similarity measures

Radial spectral energy e e

Fourier and Bayesian classifier

Alberto, Burgos Artizzu Xavier, Gonzalo, Ribbero, & Quintanilla, 2008 Mathanker et al. 2008

Double hough transformation Self developed program using Cþþ

Gee, Bossu, Jones, & Truchetet, 2008 Tangwongkit, Salokhe, & Jayasuriya, 2006

e

Two methods of segmentation and three methods of elimination Maximum likelihood, spectral angle mapper, minimum Euclidian distance, and Fisher’s linear likelihood Combination of spectral and stereoscopic bands Multi Resolution Combined Statistical and spatial Frequency Hough transform

Xavier et al. 2009

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Barley and wild oat

Color

Colour images

e

e

Fixed height

Excess green

Wavelet transform

e

Colour Image

e

Gibson et al. 2004

Piron, Leemans, Lebeau, & Destain, 2009 Sabeenian & Palanisamy, 2009 Fangming et al. 2009

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Wheat and Cutleaf Evening Primrose Cabbage, carrot and weed Cereal crops and avena weed Canola and narrow leaf weed Wheat and weed Sugarcane and weed

Statistical methods

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Crop/weed

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Table 2 e Various vegetation indices used for crop-weed discrimination by researchers. Vegetation Index/Image Vegetation

Formula

Ratio normalised difference vegetation Index (RNDVI) Modified ratio vegetation index (MRVI) Modified photochemical reflectance Index (MPRI) Normalised difference vegetation index (NDVI)

2

Reference

RNDVI ¼ (NIR eR)/(NIR þ R ) MRVI ¼ SWIR/R MPRI ¼ (G-R)/(G þ R) NDVI ¼ (NIR-R)/(NIRþR)

2-band Enhanced vegetation index (EVI2) Modified green normalised difference vegetation index (MGNDVI) Ratio vegetation index (RVI) Modified normalised difference vegetation index (MNDVI) Brightness index (BI) Red green ratio index (RGRI) Green normalised difference vegetation index (GNDVI) Normalized difference red green index (NDRGI) Normalised difference vegetation structure index (NDVSI) Ratio drought index (RDI)

EVI2 ¼ 2.5(IR e R/(IR þ R þ1) MGNDVI ¼ (SWIReG)/(SWIR þ G)

Gong et al., 2003 e Zhengwei, Patrick, & Mueller, 2008 Rouse et al., 1973, pp. 309e317, Zhang, Lan, Lacey, Hoffmann, & Huang, 2009 Jiang, Huete Alfredo, Kim, & Didan, 2007 Gitelson, Kaufman, & Merzlyak, 1996

RVI ¼ NIR/R MNDVI ¼ (SWIR e R)/(SWIR þ R)

Jordan, 1969 Rouse et al., 1973, pp. 309e317

BI ¼ G þ R þ NIR þ SWIR RGRI ¼ R/G GNDVI ¼ (NIR eG)/(NIR þ G) NDRGI ¼ (R eG)/(R þ G) NDVSI ¼ [NIR - (R þ G)  0.5]/ [NIR þ (R þ G)  0.5] RDI ¼ SWIR/NIR

Zhengwei et al., 2008 e Gitelson et al., 1996 Zhengwei et al., 2008 Zhengwei et al., 2008

Transformed NDVI (TNDVI) Green ratio vegetation Index (GRVI) Optimal soil adjusted vegetation index (OSAVI) Modified green ratio vegetation Index (MGRVI) Specific leaf area vegetation index (SLAVI) Normalised difference moisture index (NDMI)

TNDVI ¼ [(NIR-R)/(NIRþR)þ1]½ GRVI ¼ NIR/G OSAVI ¼ (NIReR)/(NIRþRþ0.16) MGRVI ¼ SWIR/G SLAVI ¼ NIR/(R þ SWIR) NDMI ¼ (IR - SWIR)/(IR þ SWIR)

Soil adjusted vegetation index (SAVI)

SAVI¼ (NIR-R)  (l þ L)/(NIR þ R þ L)

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Hunt & Rock, 1989; Pinder & McLeod 1999 Tucker, 1980 e Rondeaux, Steven, & Baret, 1996 e Lymberner et al., 2000 Gao, 1996; Shaun, Wulder, & Franklin, 2003 Zhang et al., 2009

Note: NIR ¼ Near Infra Red; R ¼ Red; SWIR ¼ Short Wave Infra Red; G ¼ Green; IR ¼ Infra Red.

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laboratory conditions or in the field. Using a spectrograph, Borregaard et al. (2000) studied beet, potatoes and various weed leaves at an early growth stage under artificial lighting. The best classification rate (89%) was obtained by using two wavelengths (694 and 970 nm) to discriminate sugar beet and weeds. For potatoes and weeds, the best classification reached 94% using 686 and 856 nm wavelengths. Feyaerts and van Gool (2001) developed a spectrograph with a low spectral resolution (35 nm) and used it in field to discriminate beets from five weed species. Wavelength selection was carried out by examining weed-crop pairs to find the wavelengths that maximised a separation function. The selected wavelengths were 441, 446, 459, 883, 924 and 988 nm with a correct classification rate of up to 83%.Wang, Zhang, Dowell, Sun, and Peterson (2001) developed an optical weed detector based on phototransistors. To select appropriate wavelengths, spectrometric measurements were performed on three weed species, wheat and soil at two growth stages, under laboratory conditions. The selected wavelengths were 496, 546, 614, 676 and 752 nm, the method used was not explicitly given. Measurements on potted plants were performed under artificial lighting. The classification rate between the weeds considered as a single group and the crop reached 72% for sufficiently densely weed infested samples. For lower infestation rates, the classification rate was below 50%. Natural light has a combination of various wavelengths; the images are formed by capturing the reflected light. To capture the light of a particular wavelength filters are to be utilised. Sometimes a specific type of filter or a series of filters are used as per requirement. In another method, artificial

light of a known wavelength is used and reflected light at receiver is utilised for further analysis. This method is used mostly for image processing work being carried out within the laboratory. To apply this technique in open fields a specific enclosure to protect the object from natural light is required. Vrindts et al. (2002) compared the results from a laboratory study on leaves using a spectrometer to in field natural lighting results. Two crops (beet and maize) and seven weed species were studied. A 100% classification rate was obtained when using nine wavelengths to discriminate maize from weeds and using eight wavelengths to separate beets from weeds. For the field spectrographic data, a stepwise discriminant approach yielded slightly lower classification rates (93% with nine spectral bands for beet and 91% using eight bands for maize). Authors found that lighting conditions strongly influenced the classification rates and the discrimination was only efficient with data taken under identical and constant lighting. Using a camera with a filter wheel holding four filters (RGB and infra-red), Franz et al. (1991a, 199b) took images of four weed species and soy leaves under controlled lighting. Using five statistical features of the leaf images, the classification rate was up to 94% with manually selected samples and taking into account the relative position of the plants and the lighting system. Zhang and Chaisattapagon (1995) selected five filters to discriminate five species of weeds from soil and wheat at two growth stages, on potted plants located under artificial lighting. Selection of the suitable filters from the available 15 filters was carried out by computing the means and coefficient of variation of the grey level ratios of all possible filter pairs.

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Hyper-spectral imaging

2.5.

Fluorescence imaging

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Hyperspectral imaging is gaining considerable interest for its application in precision agriculture (Okamoto, Suzuki, Kataoka, & Sakai, 2009). As mentioned in section 2.3 multi- spectral bands were not originally selected with species discrimination capabilities in mind, so their usefulness has been somewhat limited for weed detection in crops, even at higher resolutions. However, the focus has now shifted to determining narrower spectral bands (often called hyper-spectral imaging) that can be used specifically to detect weeds within a crop (Thenkabail et al., 2000). This has tremendous potential to enhance detection capabilities. Another notable advantage is that the portions of the electromagnetic spectrum which cannot be used with aerial systems due to atmospheric scattering or water absorption are now are available for use by sensors on the ground. Using these narrow bands, classification accuracies have often been as high as 90% for a number of different weeds in crops (El-Faki et al., 2000; Ramon et al., 2002; Koger, Bruce, Shaw, & Reddy, 2003; Henry, Shaw, Reddy, Bruce, & Tamhankar, 2004; Sapira et al. 2010). The choice of spectral resolution, and bands to include in the sensor, is primarily dependent on the variable of interest. Broadband and narrowband vegetation indices have been used to monitor various crop parameters that are often good indicators of crop yield (Shibayama & Akiyama, 1991; Thenkabail, Ward, & Lyon, 1995; Wiegand & Richardson, 1990). The difference from multi-

spectral spectroscopy is that a broader range of wavelength (more number of spectral bands) is scanned for each pixel in hyper-spectral imaging. Hyperspectral imaging based weed detection employ weed specific spectral band and appropriate statistical technique (classification algorithm) for appropriate classification. Okamoto, Murata, Kataoka, and Hata (2006) successfully applied plant classification for weed detection using hyper-spectral imaging with wavelet analysis and found out that the hyper-spectral camera can capture landscape images that include crops, weeds, and the soil surface, and can provide more extensive information than conventional red, green, and blue (RGB) images. Although RGB images consist of red, green, and blue wavebands, the obtained hyper-spectral images consist of 240 wavebands of spectral information. The image pixels of the plants (crop or weeds) were segmented from the background soil surface using Euclidean distance as the discriminant function. Further the image pixels of the crop (sugarbeet) and weeds (four species) were classified using the difference in the spectral characteristics of the plant species. In this process, classification variables were generated using wavelet transformation for data compression, noise reduction, and feature extraction, and then stepwise linear discriminant analysis was applied (Okamoto et al., 2009).

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The selection was done based on highest mean value and a smaller coefficient of variation over a number of samples. Selected wavelengths, suitable for weed detection in some crops, are given in Table 3. Each of these studies found at least one useful wavelength > 700 nm, which suggests that it was important to scan beyond the visible spectrum. Nevertheless, the selected wavelengths were highly variable, depending on the pair crop-weed to discriminate, on the methodology (measurement in laboratory controlled conditions or in the field), on the instrument used to perform the measurement (spectrograph or camera equipped with filters). In some cases, good classification results are obtained. They may be attributed to particular conditions, such as crop regularly spaced in the field with no overlapping (Borregaard et al., 2000; Feyaerts & van Gool, 2001), clearly different macrostructures of weeds and crops (Franz et al., 1991a, 1991b) or the presence of a particular colour on certain plant stems (Zhang and Chaisattapagon, 1995; El-Faki et al., 2000). An optical weed sensor was designed based on the selected wavelengths. The design principle and laboratory experiment results for this sensor were reported in Wang et al. (2001).

Fluorescence spectroscopy is a method where the fluorescence of the object of interest is measured after excitation with a beam of light (usually in the ultra-violet spectrum). UV-induced fluorescence has potential for various plant monitoring tasks (Cerovic, Samson, Morales, Tremblay, & Moya, 1999). Two types of fluorescence have been most commonly used in last two decades of the research work, I) blue-green fluorescence in about 400e600 nm range and ii) chlorophyll fluorescence in about 650e800 nm range. Laser induced fluorescence has been used to compute vegetation indices. Dedicated instruments were also developed. For example, the assessment of polyphenolic compounds in the leaf epidermis can be performed with the DualexTM instrument (Dynamax Inc., Houston, TX, USA). It is a field-portable instrument designed for the assessment of polyphenolic compounds in leaves estimated from the UV absorbance of the leaf epidermis that is measured from the double excitation (UV and red) of chlorophyll fluorescence. It is attached to leaves under field conditions (Goulas et al. 2004). Clearly, fluorescence spectroscopy is emerging as a monitoring tool in agriculture. Under UV-excitation, plants can emit a large fluorescence spectrum ranging from about 400 to 800 nm. This spectrum is the sum of two distinct types of fluorescence, the blue-green

Table 3 e Examples of studies on vegetation detection using spectroscopic techniques. Crop/weed Carrot and weed Wheat and weed Maize and weed Beet and weed Sugar Beet and weed

Suggested Band Frequencies 450 nm 480 nm 400 nm 1715 nm 660 nm

550 nm 680 nm

700 nm 1450 nm 900 nm 1925 nm 1060 nm

Light source

Year

Reference

Natural light Natural light Natural light Natural light Spectrometer

2008 1998 1994 1997 2000

Piron, Leemans, Kleynen, Lebeau, & Destain, 2008; 2009 Mack, Daniel, & Parsons, 1998 Brown et al., 1994 Vrindts et al., 2002 Borregaard et al., 2000

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a potato field while recording LED induced fluorescence at 690 nm and 740 nm (Belzile et al. 2004). Longchamps, Panneton, Samson, Leroux, and The´riault (2009) utilised the UV-induced fluorescence of green plants for maize-weed discrimination. A total of 1440 spectral signatures of fluorescence were recorded in a greenhouse from three plant groups (four maize hybrids, four dicotyledonous weed species and four monocotyledonous weed species) grown in a growth chamber. With multi-variate analysis, the full information contained in each spectrum was first reduced to scores calculated from five principal components. Subsequently, a linear discriminant analysis was applied on these scores to classify spectra on a species/hybrids basis and, subsequently, the resulting classes were aggregated according to the three plant groups. This twostep process minimised the error generated by heterogeneous groups such as dicotyledonous weeds. The output of this classification shows the significant potential of UV-induced fluorescence for plant group discrimination as the success rate reached 91.8%. No error was observed between maize and dicot weeds and most of the errors between maize and grasses came from confusion between the hybrid Pioneer 39Y85 and Setaria glauca L. (Beauv.). Analysis also determined that the position of the fluorescence sensor on the leaf and the plant age had negligible effects on the efficiency of fluorescence to discriminate plant groups.

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fluorescence (BGF) characterised by a peak around 440 nm and a shoulder around 530 nm, and the chlorophyll-a fluorescence (ChlF) in the red/far-red region with its two peaks at about 685 and 735 nm. Most of the BGF detected from intact leaves is emitted by ferulic acid, a phenolic compound bound to the cell walls in leaf epidermis and leaf veins (Lichtenthaler & Schweiger, 1998). The relative intensities of BGF and ChlF are determined by leaf intrinsic properties, notably ferulic acid and chlorophyll concentrations and also UV-transmittance of leaf epidermis. These leaf properties vary according to plant species, leaf development and environmental conditions. The relative intensities of BGF and ChlF depend also on the excitation wavelength owing to the different absorbance spectra of ferulic acid and chlorophyll. Since several intrinsic leaf properties determine plant fluorescence, the characteristics of its emission spectrum can be considered as a distinct and meaningful signature that can be used notably for plant discrimination. In one of the early studies on plant fluorescence sensing, Chappelle et al. (1985) were able to discriminate four plant groups (conifers, hardwoods, herbaceous monocotyledons and dicotyledons) based on the ratio of blue to red fluorescence intensities (F440/F685) induced by a nitrogen laser at 337 nm. Hilton (2000) observed that the F685/F735 ratio could discriminate four plant species: peas, barley, clover and Shepherd’s purse (Capsella bursa-pastoris L. Med.). Laser-induced fluorescence (LIF) is the optical emission from molecules that have been excited to higher energy levels by absorption of electromagnetic radiation. The main advantage of fluorescence detection compared to absorption measurements is the greater sensitivity achievable because the fluorescence signal has a very low background. The wavelength used is often selected to that which the species has exhibits its largest cross section. The excited species will after some time, usually in the order of few nanoseconds to microseconds, de-excite and emit light at a wavelength larger than the excitation wavelength. This light, fluorescence, is measured. The use of LIF to discriminate between crops and weeds is a relatively under researched area. Visser and Timmermans (1996) worked on the development of a series of optoelectronic weed sensors placed on a weed control prototype system. The sensors employed the chlorophyll fluorescence properties of the plants instead of the reflectance. The system has been tested in the field but no results are available. Hilton (2000) worked with LIF for discrimination between two crops and two weed species grown in the laboratory. For peas and barley with clover and Shepherds’ Purse as weeds, results showed that the ratio of chlorophyll fluorescence at 730 nm (i.e. F730) to 690 nm (F690) can be used to discriminate between crops and weeds. Furthermore, as the plants are probed with a laser signal, the system can be designed to completely isolate the effects of probing and data acquisition from the prevailing ambient conditions, a situation that is difficult to obtain with reflectance measurements and image acquisition in a vision system. ChlF induction curves coupled to a neural network was successfully used for crop-weeds discrimination (Kera¨nen, Aro1, Tyystja¨rvi, & Nevalainen, 2003). An operational fluorescence system for crop assessment was successfully used on a platform moving at 1.2 m s1 in

3.

Future directions

One of the most pressing needs for effective SSWM is for precise information on weeds, their ecosystem biology and ecology. The ability to predict weed presence and population dynamics, and its possible effect on the crop in a field based on prior history, micro-climate, topography, soil factors, agronomic cultural practices, biological and ecological characteristics of species with sensor data holds the key to success of SSWM. Both aerial and satellite imaging techniques and groundbased sensing have been used to predict the presence of weed and weed population thresholds. The decision of crop-weed discrimination is made based on the developed indexes. Some of the challenges of these techniques are: 1) the effect of field variability; 2) optimisation of the technique for a set of crop/ weed and threshold levels and 3) decision making for automation of the site-specific weed management. In most or all instances, a concurrent priority should be placed on automating the system, from data collection through to herbicide application. Ultimately, wide spread adoption will require seamless, real-time conversion of raw data into information either readily understood by the end-user and/or transferred directly to SSWM equipment. Continued research will need to focus on optimising the spectral bands used for weed detection, whether in ground-based, aerial, or satellite sensor systems. Promising results have been noted with machine vision systems and, as computational power continues to advance, research into this area will prove fruitful. The development of a more knowledgeable workforce, able to effectively use these technologies, is also critical. Spectroscopy techniques have proved to be more effective in real time weed detection, however, it requires integration to minimise field variability

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Summary and conclusion

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This paper reviews and summarises some of the techniques that have been utilised for crop-weed identification. The two major categories of weed detection are: 1) remote sensing and aerial imagery and 2) ground-based sensing. Satellite and aerial imagery techniques basically involve an image of a wider area in the visual and NIR ranges analysed over geospatial databases. The generated weed maps helps in identification of weed and application of prescribed inputs accordingly. The spectroscopy and imaging techniques examined include CCD images, fluorescence imaging, multispectral imaging and hyper-spectral imaging. This review suggests that these methods have good potential to develop threshold of weed pressures for developing control mechanism of site-specific weed management. Spectroscopic techniques have to be integrated for field variability minimisation but could be integrated into a real-time weed detection vehicle to achieve higher weed control efficiency.

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