Draft...Draft estimate the height of the plants. (Böhler, Schaepman, and Kneubühler 2018) presented a crop classification method using aerial imagery obtained by un-calibrated consumer

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Multi-Crop Recognition using UAV based High Resolution NDVI Time-Series

Journal: Journal of Unmanned Vehicle Systems

Manuscript ID juvs-2018-0036.R1

Manuscript Type: Article

Date Submitted by the Author: 08-Mar-2019

Complete List of Authors: Latif, Muhammad Ahsan; University of Agriculture Faisalabad, Computer Science

Keyword: Multi-Crop Classification, Decision Tree, Multispectral, UAV

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/juvs-pubs

Journal of Unmanned Vehicle Systems

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Multi-Crop Recognition using UAV based High Resolution NDVI Time-Series

Muhammad Ahsan Latif

Department of Computer Science, Faculty of Sciences, University of Agriculture, Faisalabad, Pakistan.

AbstractMulti-crop recognition is a highly nonlinear task in nature as it involves many dynamic factors to

address. In this paper, a decision tree (DT) based approach is presented to classify and recognize

17 different crops. High spatial and temporal NDVI signatures were extracted from multispectral

imagery using multispectral sensor onboard quadrotor. Detailed datasets were prepared through

sampling based on normal distribution with different standard deviations. The impact of reduced

dimensions was also tested using principal component analysis. A very high degree of accuracy

is achieved for classification. The results also indicate that NDVIs pertaining to early-to-mid

season have much more weightage in the classification process for multiple crops.

Keywords: Crop classification; UAV; Decision Tree; NDVI

1. IntroductionThe farming systems in most of the Asian regions are generally characterized by small field size,

diverse crop species with similar traits and intercropping. These small scale farming approaches

lead to produce complex cropping patterns difficult to map through satellite based traditional

remote sensing techniques. The site-specific crop mapping is important as it helps the

agronomists to develop estimations for yield, biomass, vigor, crop cover, etc. for multiple crops

discretely. Nevertheless, mapping multiple crops within a small area is challenging as many

crops exhibit almost similar color hue and plant phenology, overlapping spectral signatures,

similar growth cycles and patterns. These dynamics contribute strenuously making multi-crop

mapping difficult. With this backdrop, this contribution presents a methodology for site-specific

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multi-crop recognition using a high resolution multispectral sensor onboard a quadcopter UAV

(unmanned air vehicle).

Recognizing multiple crops through classification approaches has been a key area of interest for

satellite based remote sensing (Huang et al. 2018; Bargiel 2017; Wu et al. 2017; Hütt et al. 2016;

Hao et al. 2015). In case of UAVs, however, very limited literature over multi crop classification

is available. For example, (Rebetez 2016) proposed a hybrid method based on deep neural

network for crop classification using RGB image dataset. The computationally expensive

approach used texture patterns and color distribution to classify a wide variety of crops. For

delineation and classification of maize crop, (Hall et al. 2018) developed object-oriented

approaches coupled with image texture, hue, saturation and intensity using RGB and NIR

imagery aerially acquired through a quadcopter. To differentiate sugar beet plants and weeds,

(Lottes et al. 2017) exploited spatial arrangement of plants through geometric features and

random forest approach using two digital cameras (RGB, RGB + NIR) onboard a quadcopter.

(Lin et al. 2019) proposed a Fourier dense network based on convolutional neural network to

classify the plants. The methodology involves learning and extraction of the plant features in

time and frequency domains from the optical images captured using a UAV. (Liu 2018)

proposed a multistep process to detect an invasive parsnip plant using a simple RGB camera

fixed on a UAV. The results provided evidence that UAVs with only visible band cameras serve

as economic resource for small and irregular vegetation detection. Through convolution neural

network approach, (Wei and Mao 2018) recognized rice crop using UAV imagery. An object

based mangrove classification methodology is presented by (Cao et al. 2018) that involves

hyperspectral and RGB sensors onboard UAV. They extracted texture features, spectral features

and vegetation indices from hyperspectral imagery whereas digital surface models were used to

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estimate the height of the plants. (Böhler, Schaepman, and Kneubühler 2018) presented a crop

classification method using aerial imagery obtained by un-calibrated consumer grade cameras

fixed on a UAV. They evaluated different spectral and spatial resolutions with different structure

elements to map textural features. The optimal classification was achieved with RGB bands and

textural features extracted at 0.5 m resolution. (Liu et al. 2018) used an integrated approach by

combining digital surface model and texture features from RGB images in SVM (support vector

machine) classification framework. An object based technique was tested for classification of

crops and an accuracy 85% was achieved as reported by (Park and Park 2015). The other closely

related tasks widely performed through UAVs include crop-weed separation or weed detection

(Zisi et al. 2018; Pérez-Ortiz et al. 2016; López-Granados et al. 2016; Hung et al. 2014) and land

cover usage estimation (Hyeok and Wan 2017; Feng et al. 2015). A comprehensive insight into

the UAV based crop phenotyping is also discussed by (Yang et al. 2017) and (Latif 2018)

Following the Introduction sec., this paper is divided into three main parts, i.e., Materials and

Methods, Results and Discussion. The Materials and Methods sec. further encapsulates sub-

sections such as site description, image acquisition, data sampling, classification and dimension

reduction.

2. Materials and Methods2.1. Site Description

The study was conducted at main agronomy research farm, University of Agriculture Faisalabad,

Punjab, Pakistan. A plot of size 35 m x 18 m (182m, Latitude: N 31°26'29.75'', Longitude: E

73°4'23.33'') with 17 different crops was selected for aerial operations as shown in Figure 1. The

plot contained 17 subplots each of size 18 m x 1 m with a gap of 1 m between adjacent subplots.

Each subplot contained 4 rows of the same crop with row-to-row distance of 0.3 m. The detail of

the crops observed is provided in Table 1.

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2.2. Image Acquisition and ProcessingWe used a quadrotor (Phantom-4, DJI) platform for aerial operations. To work for precision

agriculture, Phantom-4 platform was integrated with a customized payload (Figure 2). This

included a multispectral camera (ADC Micro, Tetracam Inc., Chatsworth, CA, USA),

FirePointTM 100 GPS and a battery. The GPS module and battery were attached directly with the

landing gears of the quadrotor. The ADC-Micro sensor was fixed to the quadrotor faced

downwards through a light-weight frame made up of aluminum alloy. The ADC-Micro sensor is

a multispectral sensor with three fixed bands (Red-Green-NIR) equivalent to the Landsat

thematic bands TM2, TM3 and TM4. It contains a yellow long-pass filter permanently fixed in

front of the lens which blocks the blue light and allows the remaining visible and NIR bands pass

through the lens. Inside the camera, there is an Aptina CMOS sensor and a Bayer RGB filter

array with a checkerboard pattern. The highly light-weighted (90 g) sensor operates within the

wavelength range of 520 nm - 920 nm and is very suitable for remote sensing applications in

agriculture. The ADC-Micro sensor was pre-set to capture multispectral images at nadir position

in an uncompressed raw-10 format. Total, 8 flights were conducted after every two weeks at an

altitude of 50 m AGL (Above Ground Level) with ground sampling distance of 2 cm (spatial

resolution). The flight operations were executed around mid-day to avoid the impact of shadows.

The calibration for IMU (Inertial Measurement Unit) and compass was performed just once at

the site before the very first flight operation and was never required at any later stage during

whole season. As the pixel values are direct manifestation of reflectance that changes

continuously, it is very important to establish the right association between the two measures to

produce true exhibition of vegetation. To achieve this, we used a Teflon calibration target which

gives 100% reflectance for the electromagnetic wavelength range 400 nm – 1000 nm. This

spectrally flat response helps to determine in-situ spectral-balance of sunlight and is

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subsequently used to make corrections in NIR/R/G bands. This information was later used in the

post-processing of multispectral images to calculate NDVI values (equation 1). The image

coordinates were corrected using the calibration parameters given in Table 2 to perform certain

geometrical transformations, i.e., rotational, projective and shear. The rectified multispectral

images were further segmented into two groups, i.e., the crop pixels and soil pixels. The quality

of segmentation process is very crucial to ensure that only the contribution from pixels

representing the canopies is considered while deriving NDVI. The classification is based on Red

and NIR reflectance exposed by the image content. The vegetation part shows high NIR

reflectance and the soil parts (also background, shadows, etc.) reveal higher red values. To derive

NDVI values from the same region(s), a fixed area of interest (1 m2) was marked in the middle

of each sub-plot.

Normalized Difference Vegetation Index (NDVI) = (NIR - RED) / (NIR + RED) (1)

For crop monitoring and vegetation studies, NDVI is the most widely accepted vegetation index

(Collwell 1974). It is an effective indicator as it is robust to atmospheric corrections and

variations in sun-light. According to the Köppen-Geiger climate classification system, Faisalabad

region has been categorized as a hot desert climate zone (Peel, Finlayson, and Mcmahon 2007).

The climate plays a key role in defining seasonal-growth and phenology of the crops. Therefore,

different climate-variables were also recorded in the meteorological observatory (CR-1000,

Campbell Scientific, Utah, USA) installed at University of Agriculture, Faisalabad as shown in

Figure 4.

2.3. Data Sampling As mentioned above, a fixed area of 1 m2 was marked in the middle of each crop subplot for

aerial data acquisition during whole season. The canopy pixels (≈ 3000) inside each fixed area

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were averaged to produce a single NDVI value for a particular crop on a specific date. Following

the same procedure, NDVI values for all 17 crops were acquired on 8 different days after regular

intervals covering almost complete phenological phases. Each crop sample is thus represented by

a distinct NDVI vector of 8 scalar values. These NDVI vectors were repeatedly checked and

validated ensuring their true respective representation. Each NDVI vector can be assumed as

NDVI signature for a particular crop. To train a DT classifier for crop classification, a dataset

with considerable number of samples is required. Moreover, despite our utmost care while

preparing NDVI vectors for different crops listed in the experiment, there is always room for

variations in NDVI values as a symbol of generalization. These issues were addressed by

modeling NDVI vectors through normal distribution as given in following.

(2)𝑓(𝑥│𝜇,𝜎) = 1

𝜎 2𝜋𝑒―(𝑥 ― 𝜇)2

2𝜎2

Where ‘µ’ represents mean NDVI value for one crop on a specific day. There are 8 mean NDVI

values for each NDVI vector (crop). The standard deviation ‘σ’ is used to define the scale of

variation for NDVI values. Each NDVI vector sample generated this way takes the following

form:

(3)(𝑋,𝑌) = (𝑥1, 𝑥2,………,𝑥𝑘, 𝑌)

‘X’ is the set of NDVI vectors where each vector sample contains 8 predictors (k=8), i.e., 8

NDVI scalar values for 8 different days. ‘Y’ represents the actual class assigned to NDVI

vectors, i.e., crop label.

2.4. ClassificationTo classify 17 different crops based on high resolution NDVI time series, we used decision tree

classification approach (Rokach and Maimon 2014) as it reflects an easy to understand close

human decision making logic than other approaches (James et al. 2013). The DT algorithm

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breaks down a dataset into smaller subsets whilst a tree structure is incrementally developed. The

optimal split decisions are made at nodes considering one feature at one time. The algorithm

continues to perform splitting until pure nodes are reached. The topmost node is regarded as the

best predictor whereas final classification is exhibited by leaf nodes. In our case, the dataset is

comprised of NDVI vectors formulated against different crops on specific dates. The mean

NDVI values inside these NDVI vectors serve as diverse set of features which form the basis for

splitting nodes. We used different splitting criteria to test the robustness of decision tree as given

below (Jost 2006) .

Gini’s Diversity IndexGini’s index is a measure of impurity at the node(s). ‘p(i)’ represents the fraction of

samples labeled with class ‘i’ at any node. A node with samples belonging to only one

class has a Gini’s index as 0. Any other state leads to a positive index value.

(4)𝐺𝑖𝑛𝑖′𝑠 𝐼𝑛𝑑𝑒𝑥 = 1 ― ∑𝑖𝑝

2(𝑖) Deviance

With same definition of p(i) as described for Gini’s index, the deviance splitting rule has

following form. For a pure node, deviance is 0 and positive otherwise.

(5)𝐷𝑒𝑣𝑖𝑎𝑛𝑐𝑒 = ― ∑𝑖𝑝(𝑖)𝑙𝑜𝑔 𝑝(𝑖)

Twoing RuleTwoing splitting rule will maximize the following change of impurity measure:

(6)∆𝑖(𝑡) = 𝑃𝐿𝑃𝑅

4 [∑𝐽𝑗 = 1|𝑝(𝑗│𝑡𝐿) ― 𝑝(𝑗│𝑡𝑅)|]

2

PL and PR are the proportions of samples that split to the left and right respectively. A

relatively larger expression gives pure child node whereas a smaller expression leads to

produce similar child nodes thus decreases node purity.

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2.5. Dimension ReductionIn a multidimensional system like ours, different variables may lead to produce redundant

information. This happens when some variables move together under some hidden correlation. In

our case, the samples are shaped as NDVI vectors each with 8 mean NDVI values calculated

against 8 different dates for each crop. By removing redundancy information in the data, we can

figure out NDVI values against the dates which contribute significantly in classification process.

This is achieved through principle component analysis (Jolliffe 2002). The approach transforms

original multidimensional space into a new space where all the dimensions are orthogonal to

each other. The orthogonality eliminates the redundancy in the original data. Each new

dimension is referred to as principal component and can be written as a linear combination of the

original variables. The first principal component gives maximum variance, the second principal

component contains second highest variance in the data and hence forth. The matrix of

eigenvalues calculated as measure of variance for principle components is given below in

equation 8. The analysis shows only 4 features (mean NDVIs) out of total 8 features are enough

to define >95% variance (for σ = 0.02).

(7)𝑃𝐶 = ∑𝑎𝑖𝑥𝑖

Equation 7 defines principle component as a linear combination of original variables. Here ‘ai’

indicates ith coefficient and xi is ith mean NDVI value.

1 15 1 15 1 15 1 15

4

4

4

4

0.043 0 0 0 0 0 0 00 0.009 0 0 0 0 0 00 0 0.005 0 0 0 0 00 0 0 0.002 0 0 0 00 0 0 0 9.07 0 0 00 0 0 0 0 5.75 0 00 0 0 0 0 0 4.11 00 0 0 0 0 0 0 2.43

Jan Jan Feb Feb Mar Mar Apr AprNDVI NDVI NDVI NDVI NDVI NDVI NDVI NDVI

ee

ee

(8)

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3. Results Figure 5 shows classification of all 17 crops in the form of a classification tree. The tree is

comprised of 16 decision nodes and 17 leaf nodes. The decision nodes show the variables' names

used in decisions for classification. These are the mean NDVI values calculated on Jan 1, Jan 15,

Feb 1, Feb 15, Mar 1, Mar 15, Apr 1 and Apr 15. The leaf nodes represent classified crops, i.e.,

the crops as a result of decisions based on mean NDVI values imposed at decision nodes. The

labels of these classes at leaf nodes are A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P and Q. The

actual names of these crop labels are given in Table 1. NDVI - Jan 15 is used at three decision

nodes, i.e., it classifies crop ‘P’ and node NDVI – Jan 1, sub node NDVI – Jan 1 and node

NDVI – Feb 1, and, crop ‘L’ and sub node NDVI – Feb 1. NDVI – Jan 1 appears at five nodes,

i.e., it classifies crop ‘Q’ and node NDVI – Jan 15, crops ‘A’ and ‘J’, crops ‘H’ and ‘G’, crop ‘E’

and sub node NDVI – Jan 15, and, crop ‘M’ and sub node NDVI – Feb 1. NDVI – Feb 1 is used

at four decision nodes, i.e., it classifies sub nodes NDVI – Feb 1 and NDVI – Feb 15, sub nodes

NDVI – Jan 1, crops ‘D’ and ‘I’, and crops ‘N’ and ‘O’. The node NDVI – Feb 15 breaks down

into further two sub nodes, i.e., NDVI – Mar 1 and NDVI – Mar 15. The crops ‘F’ and ‘K’ are

classified by NDVI – Mar 1 whereas crops ‘B’ and ‘C’ are classified by NDVI – Mar 15. No

crop is classified by NDVI – Apr 1 or NDVI – Apr 15. The respective confusion matrix given in

Table 3 shows very few misclassifications culminating 99.9% accuracy. Different splitting

criteria described above produced similar classification tree. This shows the robustness in NDVI

vector dataset used in the classification process. The original dataset is composed of 8 features,

i.e., each sample contains 8 mean NDVI values. To reduce dimensions of data, PCA resulted

only four principal components offering >95% variance. Table 5 shows coefficients and total

variance for four selected principal components. The coefficients indicate that mean NDVI

values pertaining to early to mid-season contribute significantly in crop classification process

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than remaining mean NDVI values. The confusion matrix for classification tree based on

selected features identified through PCA is shown in Table 4. Here, we see classification

accuracy of 99.4% with certain misclassifications mentioned. It is observed that though

classification using all 8 features is more accurate than using only selected features, nevertheless

it is still very optimal. Figure 6 shows the relation between predictive accuracy of classification

models and classification error (a measure of misclassification) as standard deviation varies. Two

different models are tested, i.e., the model using all 8 features and the model with only 4 features

selected through PCA. It is clear that predictive accuracy of both models (DT and PCA based

DT) decreases and respective classification errors (CE) increases as standard deviation (Sigma)

increases. It is observed that predictive accuracy decreases from 100% to ≈76% as standard

deviation varies from 0.01 to 0.06. The classification error increases from 0 to ≈0.25 for same

value range of standard deviation. This helps in choosing an optimal value of standard deviation

to develop decision tree classification model.

4. DiscussionThe UAVs facilitated acquisition of high resolution image data to a great extent for applications

in precision agriculture. The flexibility, availability and affordability inherent to the said

technology has really outclassed the traditionally used satellite imagery for site-specific precision

farming (Stein 2018). The high resolution crop imagery acquired through UAVs provides an

easy access to understand and exploit the fine detail at plant level. In this work, we presented

multi crop classification based on high resolution NDVI data using multispectral sensor onboard

a quadrotor. Many crops share similar visible features in terms of color, shape, growth, etc.,

making crop identification difficult. We choose a plot with 17 different crops for our experiment.

We performed 8 flight operations on different days with a gap of 15 days regularly. This led to

calculate mean NDVI values and NDVI vectors for each crop. The provision of high resolution

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spatial and temporal NDVI time series data laid the foundation for crop classification as results

revealed. Based on NDVI vectors, data samples are generated using normal distribution.

Standard deviation (σ) is used to define the scale for NDVI values to vary within a certain range

against a definite date. Specific NDVI values can’t be attributed to phenological phases of crops

discretely as there are many factors that affect NDVI values, e.g., weather, water stress, nutrient

deficiency, calibration error(s), image noise, soil reflectance, etc. The variations in NDVI values

due to these factors are covered by defining a scale of variation using standard deviation. We

tested standard deviation for a range of values, i.e., from 0.01 to 0.06. For σ = 0.01, the generated

NDVI samples offer an idealistic crop classification using DT algorithm. However, this can be

the result of overfitting as training data doesn’t contain generality. For σ = 0.02 or σ = 0.03, the

modeled NDVI values offer a rather realistic sampling data. The training data turns out to be

more general as NDVI values vary within an optimal interval. For σ >= 0.05, overlapping within

NDVI values against different dates increases that causes to grow misclassifications. A close

look at DT classifier shows NDVI values related to early to mid-season contributed more

significantly for multi crop classification than other NDVIs. For example, NDVIs against Jan 1,

Jan 15 and Feb 1 made frequent appearance at different decision nodes. This fact gives a key

finding that early season NDVIs play pivotal role in classification and recognition of multiple

crops using high resolution aerial imagery. This also concludes that NDVIs vary considerably

during that specific time enabling multi crop recognition easy. The PCA based dimension

reduction also verifies the major contributions from specific NDVIs, e.g., 1st principle

component contains 69.69% variance that mainly came from NDVIs against Jan 1, Jan 15, Feb 1

and Feb 15. Using selected features (NDVIs) only, PCA based confusion matrix indicates an

optimal classification result with 99.4% accuracy. This is slightly less than the accuracy we

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achieved using all features. The results suggest that high spatial and temporal resolution NDVI

data may serve a primary source for site specific crop recognition. For future work, we intend to

include other important crop traits such as plant height, plant shape and color in the classification

process to make better multi crop recognition. For this, multi view crop imagery will be

exploited to develop 3D models and feature extraction. The use of hyperspectral sensors may

also be used to get more precise high resolution spatial and temporal NDVI signatures for multi

crop classification and recognition.

5. Acknowledgement The support from the Endowment Fund Secretariat, University of Agriculture, Faisalabad is

deeply acknowledged for this research work.

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Figure Captions

Figure 1. Experimental plot with 17 different crops labeled

Figure 2. Phantom-4 DJI with payload

Figure 3. Classification of pixels for one image (canopy vs soil)

Figure 4. Climate-variables observed during experiment-phase (2016-17)

Figure 5. Classification tree for 17 crops with NDVI values at nodes used for decisions (σ=0.02)

Figure 6. Change in models’ accuracy and classification errors as sigma varies

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Figure 1. Experimental plot with 17 different crops labeled

Figure 2. Phantom-4 DJI with payload

Figure 3. Classification of pixels for one image (canopy vs soil)

N

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Draft Figure 4. Climate-variables observed during experiment-phase (2016-17)

Figure 5. Classification tree for 17 crops with NDVI values at nodes used for decisions (σ=0.02)

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DraftFigure 6. Change in models’ accuracy and classification errors as sigma varies

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Table 1 List of crops studied

Sr. no. Label Crop Name Seasonal Time (Punjab-Pakistan)

1 A Wheat Nov-April2 B Barley Nov-April3 C Oat Nov-April4 D Clover Nov-April5 E Alfalfa Nov-April6 F Rapeseed Nov-April7 G UAF-11 Rapeseed Nov-April8 H Mustard Nov-April9 I Linseed Nov-April10 J Kusumba Nov-April11 K Haloon Nov-April12 L Methre Nov-April13 M Lentil Nov-April14 N Chickpea Nov-April15 O Fennel Nov-April16 P Soo ye Nov-April17 Q Black cumin Nov-April

Table 2. Lens calibration parameters of ADC-Micro sensor

Principal Distance Optical Center Radial Distortion Differential

ScalingPixel Size

(mm)

8.5097 xp= 0.0593, yp= -0.0655 K1=1.18e-3, K2=1.65e-5, K3=-1.85e-6 b1=0, b2=0 0.0032

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Table 3. DT based Confusion matrix for classification with 8 features (99.9%)

A 499 1B 500C 1 499D 500E 500F 500G 500H 500I 500J 500K 1 499L 500M 499 1N 1 499O 500P 500Q 500

A B C D E F G H I J K L M N O P Q

Table 4. DT based Confusion matrix for classification with 4 features (99.4%)

A 496 4B 500C 3 497D 476 17 3 4E 11 488 1F 500G 499 1H 3 497I 500J 500K 500L 500M 500N 500O 3 497P 500Q 500

A B C D E F G H I J K L M N O P Q

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Table 5. Coefficients and variance of 4 principal components

PrincipalComponents

NDVI(Jan 1)

NDVI(Jan 15)

NDVI(Feb 1)

NDVI(Feb 15)

NDVI(Mar 1)

NDVI(Mar 15)

NDVI(Apr 1)

NDVI(Apr 15) Variance (%)

PC1 0.26 0.55 0.59 0.34 0.07 -0.04 -0.30 -0.23 69.69PC2 0.03 0.28 0.21 0.19 -0.03 0.21 0.68 0.56 15.04PC3 -0.38 -0.15 -0.08 0.49 0.61 0.33 -0.23 0.16 7.94PC4 0.33 0.16 -0.25 -0.17 0.30 0.55 0.31 -0.51 3.9

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Draft...Draft estimate the height of the plants. (Böhler, Schaepman, and Kneubühler 2018) presented a crop classification method using aerial imagery obtained by un-calibrated consumer