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Katholieke Universiteit. LEUVEN. Spectral Weed Detection and Precise Spraying. Laboratory of AgroMachinery and Processing Els Vrindts, Dimitrios Moshou, Jan Reumers Herman Ramon, Josse De Baerdemaeker. Research sponsored by IWT and the Belgian Ministry of Small Trade and Agriculture.

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Spectral weed detection and precise spraying

Katholieke Universiteit

LEUVEN

Spectral Weed Detectionand Precise Spraying

Laboratory of AgroMachinery and Processing

Els Vrindts, Dimitrios Moshou, Jan ReumersHerman Ramon, Josse De Baerdemaeker

Research sponsored by IWT and the Belgian Ministry of Small Trade and Agriculture


Overview
Overview

  • Spectral measurements of crops and weeds

    • in laboratory

    • in field

  • Processing of spectral data with neural networks

  • Precise spraying


Optical d etection of w eeds
Optical detection of weeds

Techniques

  • red/NIR detectors (vegetation index)

  • image processing (color, texture, shape)

  • remote sensing of weed patches

  • reflection in visible & NIR light

    different detection possibilities, different scales

    Requirements for on-line weed detection:

  • fast & accurate weed detection

  • synchronized with treatment


Spectral weed detection
Spectral weed detection

Factors affecting spectral plant signals

  • leaf reflection, dependent on species and environment, stress, disease

  • canopy & measurement geometry

  • light conditions

  • detector sensitivity


Spectral analysis of plant leaves in laboratory

Laboratory measurements

integrating sphere

sample

computer

spectrophotometer

Spectral analysis of plant leavesin laboratory

Diffuse Reflectance Spectroscopy of Crop and Weed Leaves


Diffuse reflectance of a leaf

Laboratory measurements

Diffuse Reflectance of a Leaf


Spectral dataset

Laboratory measurements

Spectral Dataset


Reflectance of c rop and w eed l eaves

Laboratory measurements

Reflectance of crop and weed leaves


Spectral analysis

Laboratory measurements

Spectral analysis

  • stepwise selection of discriminant wavelengths

  • multivariate discriminant analysis, based on reflectance response at selected wavelengths (dataset a)

    • assuming multivariate normal distribution

    • quadratic discriminant rule

      classes with different covariance structure

  • testing the discriminant function: classification of spectra from dataset b


Spectral r esponse of b eet w eeds

Laboratory measurements

Spectral response of beet & weeds


Spectral r esponse of m aize w eeds

Laboratory measurements

Spectral response of maize & weeds


Spectral r esponse of p otato w eeds

Laboratory measurements

Spectral response of potato & weeds


Classification r esults

Laboratory measurements

Classification results


Field measurement of crop and weeds

Field measurements

Field measurement of crop and weeds

Signal path

Processingmethod

Variation inlight condition

Detector sensitivity

Measurement geometry


Equipment for field measurement

Field measurements

Equipment for field measurement

spectrograph + 10-bit CCD, digital camera,

computer,

12 V battery and

transformer

on mobile platform


Equipment spectrograph

Field measurements

Equipment - Spectrograph

both spatial and spectral information in images


Image data

Field measurements

spectral

axis

spatial axis

Image data

  • maize, sugarbeet, 11 weeds

  • 2 different days, different light conditions

  • 755 x 484 pixels


Spectral response of sensor

Field measurements

Spectral response of sensor


Data processing

Field measurements

Data processing

  • spectral resolution: 0.71 nm /pixel

  • plant/soil discrimination with ratio: NIR (745 nm) / red (682 nm)

  • data reduction by calculating average per 2.1 nm, removing noisy ends

  • resulting spectra: 484.8 - 814.6 nm range, 2.1 nm step

  • independent datasets of maize, sugarbeet and weeds


Spectral datasets

Field measurements

Spectral datasets


Mean canopy reflections

Field measurements

Mean canopy reflections


Canonical analysis of sugarbeet weeds

Field measurements

Canonical analysis of Sugarbeet - weeds


Canonical analysis of maize weeds

Field measurements

Canonical analysis of Maize - weeds


Discriminant analysis sugarbeet

Field measurements

Discriminant analysis Sugarbeet


Discriminant analysis maize

Field measurements

Discriminant analysis Maize


Graphic comparison datasets

Field measurements

Graphic comparison datasets


Graphic comparison datasets1

Field measurements

Graphic comparison datasets


Graphic comparison datasets2

Field measurements

Graphic comparison datasets


Discriminant analysis ratios sugarbeet

Field measurements

Discriminant analysis ratiosSugarbeet


Discriminant analysis ratios maize

Field measurements

Discriminant analysis ratiosMaize


Results

Field measurements

Results

  • only spectral info (485-815 nm)

  • classification based on narrow bands in discriminant functions

    • good results in similar light and crop conditions

    • large decrease in performance for other light conditions

  • using ratios of narrow bands

    • improvement, but not sufficient


Improving results

Field measurements

Improving results

  • influence of light conditions

    • adaption of classification rule

      • determining light condition and applying appropriate calibration/LUT

    • spectral inputs that are less affected by environment

      • measuring irradiance, calculating reflectance

    • other classification methods


Neural network for classification

Crop-weed classification

Neural network for classification

  • Comparison of different NN techniques for classification

  • Self-Organizing Map (SOM) neural network for classification

    • used in a supervised way for classification

    • neurons of the SOM are associated with local models

    • achieves fast convergence and good generalisation.


Neural network for classification1

Crop-weed classification

x

x

x

x

class

Neural

lattice

(A)

Input Layer

1

2

n

-1

n

Distribution

weights

Layer

second hidden

layer

….

Pattern Layer

first hidden

Input

Space

(V)

….

layer

O

Summation Layer

O

1

f

(

x

)

f

(

x

)

n

1

2

s1(k)

s2(k)

s3(k)

s4(k)

Decision Layer

Output Layer

Neural network for classification

SOM

MLP

PNN

  • ADVANTAGES

  • Learns with reduced

  • amounts of data

  • Fast Learning

  • Visualisation

  • Retrainable

  • DISADVANTAGES

  • Discrete output

  • ADVANTAGES

  • Good extrapolation

  • DISADVANTAGES

  • Slow Learning

  • Local minima

  • Needs a lot of data

  • ADVANTAGES

  • Fast Learning

  • Retrainable

  • DISADVANTAGES

  • Needs all training data

  • during operation

  • Needs a lot of data


Comparison between methods

Crop-weed classification

Comparison between methods

MLP: Multi-Layer Perceptron, PNN: Probabilistic N Network, SOM: Self-Organizing Map, LVQ: Learning Vector Qantization, LLM: Local Linear Mapping

Moshou et al., 1998, AgEng98, Oslo

Moshou et al., 2001, Computers and Electronics in Agriculture 31 (1): 5-16


Crop-weed classification

Comparison between methods

MLP: Multi-Layer Perceptron, PNN: Probabilistic N Network, SOM: Self-Organizing Map, LVQ: Learning Vector Qantization, LLM: Local Linear Mapping


Crop-weed classification

Comparison between methods

MLP: Multi-Layer Perceptron, PNN: Probabilistic N Network, SOM: Self-Organizing Map, LVQ: Learning Vector Qantization, LLM: Local Linear Mapping


Conclusions on llm som technique

Crop-weed classification

Conclusions on LLM SOM technique

  • The strongest point is the local representation of the data accompanied by a local updating algorithm

  • Local updating algorithms assure much faster convergence than global updating algorithms (e.g. backpropagation for MLPs)

  • Because of the topologically preserving character of the SOM, the proposed classification method can deal with missing or noisy data, outperforming “optimal” classifiers (PNN)

  • The proposed method has been tested and gave superior results compared to a variety statistical and neural classifiers


Precision spraying through controlled dose application

Precision treatment

Precision spraying through controlled dose application

Unwanted variations in dose caused by horizontal and vertical boom movements


Active horizontal stabilisation of spray boom

Validation with ISO 5008 track

movement of spray boom tip with and without controller

Precision treatment

0.4

0.3

0.2

0.1

Distance (m)

0

-0.1

-0.2

-0.3

0

5

10

15

20

25

30

Time (s)

Active horizontal stabilisation of spray boom


Vertical stabilisation of spray boom

Precision treatment

reduction

fixing between plates

electric motor

g

frame connected to tractor

rol

q

boom

cable

ultrasonic sensors

Vertical stabilisation of spray boom

Slow-active system for slopes

Resulting boom movement


On line selective weed treatment

Precision treatment

On-line selective weed treatment

Indoor test of on-line weed detection and treatment


Indoor test of on line weed detection and treatment

Precision treatment

Indoor test of on-line weed detection and treatment

  • Sensor: Spectral line camera

  • Classification: Probabilistic neural network

  • Program in Labview with c-code

    • Image acquisition frequence: 10 images/sec, travel speed: 30cm/sec, segmentation with NDVI ( > 0.3)

    • Off-line training of NN, On-line classification

    • Decision to spray:

      > 20 weed pixels and > 35% of vegetation is weed

  • Spray boom with PWM nozzles and controller, provided by Teejet Technologies


Indoor test of on line weed detection and treatment1

Precision treatment

Indoor test of on-line weed detection and treatment

Color image and spectral image


Indoor test results

Precision treatment

Indoor test - Results

  • Comparison of nozzle activation with weed positions


Indoor test results1

Precision treatment

Indoor test - Results

  • separate weed classes (4) did not improve crop-weed classification

  • Correct detectionof nearly all weeds

  • Only 6 % redundant spraying of crop

  • Up to 70 % reduction of herbicide use

Experimental set up

camera

nozzle

weed


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