Forecasting future technological needs for rice crop in India Questionnaire - PowerPoint PPT Presentation

Loading...

PPT – Forecasting future technological needs for rice crop in India Questionnaire PowerPoint presentation | free to view - id: 743e7e-YzQ4O



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Forecasting future technological needs for rice crop in India Questionnaire

Description:

... year growth model Choice of explanatory variables Relevant weather variables appropriate lag periods depending on life cycle Crop stage ... Phytophthora blight ... – PowerPoint PPT presentation

Number of Views:85
Avg rating:3.0/5.0
Slides: 75
Provided by: ft65
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Forecasting future technological needs for rice crop in India Questionnaire


1
Pests and Diseases Forewarning System
Amrender Kumar
Scientist Indian Agricultural Statistics
Research Institute, Library Avenue, New Delhi,
INDIA akjha_at_iasri.res.in
2
Crop Pests - Weather Relationship
Crop
Weather
Pests
3
  • Diseases and pests are major causes of reduction
    in crop yields.
  • However, in case information about time and
    severity of outbreak of diseases and pests is
    available in advance, timely control measures can
    be taken up so as to reduce the losses.
  • Weather plays an important role in pest and
    disease development.
  • Therefore, weather based models can be an
    effective scientific tool for forewarning
    diseases and pests in advance.

4
Why pests and disease forewarning
  • Forewarning / assessment of disease important
    for crop production management
  • for timely plant protection measures
  • information whether the disease status is
    expected to be below or above the threshold level
    is enough, models based on qualitative data can
    be used qualitative models
  • loss assessment
  • forewarning actual intensity is required -
    quantitative model

5
Variables of interest
  • Maximum pest population or disease severity.
  • Pests population/diseases severity at most
    damaging stage i.e. egg, larva, pupa, adult.
  • Pests population or diseases severity at
    different stages of crop growth or at various
    standard weeks.
  • Time of first appearance of pests and diseases.
  • Time of maximum population/severity of pests and
    diseases.
  • Weekly monitoring of pests and diseases progress.
  • Occurrence/non-occurrence of pests diseases.
  • Extent of damage.

6
Data Structure
Historical data at periodical intervals for 10-15
years
Year Observation Observation Observation Observation Observation Observation Observation
Year 1 2 3 4 . . .
1 y11 y12 . . . . .
2 y21 y22 . . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .
10-15 . . . . . . .
7
  • Historical data for 10-15 years at one point of
    time
  • overall status
  • disease intensity
  • crop damage.

8
  • Data for 5-6 years at periodic intervals
  • For week-wise models, data points inadequate
  • combined model for the whole data in two steps
  • Data at one point of time for 5-6 years
  • Model development not possible
  • Qualitative data for 10-15 years
  • Qualitative forewarning
  • Occurrence / non-occurrence of disease
  • Mixed data conversion to qualitative
    categories
  • Data collected at periodic intervals for one year
  • Within year growth model

9
  • Choice of explanatory variables
  • Relevant weather variables
  • appropriate lag periods depending on life cycle
  • Crop stage / age
  • Natural enemies
  • Starting / previous years last population of
    pathogen

10
Forecast Models
  • Between year models
  • These models are developed using previous years
    data.
  • The forecast for pests and diseases can be
    obtained by substituting the current year data
    into a model developed upon the previous years.
  • Within year models
  • Sometimes, past data are not available but the
    pests and diseases status at different points of
    time during the current crop season are
    available.
  • In such situations, within years growth model
    can be used, provided there are 10-12 data points
    between time of first appearance of pests and
    diseases and maximum or most damaging stage.
  • The methodology consists of fitting appropriate
    growth pattern to the pests and diseases data
    based on partial data.

11
  • Thumb rules
  • Most common
  • Extensively used
  • Judgment based on past experience with no or
    little mathematical background
  • Example
  • A day is potato late blight favorable if
  • the last 5 - day temperature average is lt 25.50
    C
  • the total rainfall for the last 10 days is gt
    3.0 cm
  • the minimum temperature on that day is gt 7.20 C
  • Trivedi et al. (1999)

12
  • Regression models
  • Relationship between two or more quantitative
    variables
  • The model is of the form
  • Y ?0 ?1 X1?2 X2 . ?p Xp e ,
  • where
  • ?is are regression coefficients
  • Xis are independent variables
  • Y variable to forecast
  • e random error
  • Variables could be taken as such or some suitable
    transformations

13
  • Cotton
  • of incidence of Bacterial blight (Akola)
    Weekly models (42nd to 44th SMW)
  • Data used 1993-1999 on MAXTemp, MINTemp, RH1
    (morn), RH2 (aft) and RF X1 to X5) lagged by
    2 to 4 weeks
  • Model for 44th SMW
  • Y 133.18 - 3.09 RH2L4 1.68 RFL4 (R20.78)

14
(No Transcript)
15
  • Potato
  • Potato aphid is an abundant potato pest and
    vector of potato leaf-roll virus, potato virus Y
    , PVA, etc.
  • Potato aphid population Pantnagar (weekly
    models)
  • Data used 1974-96 on MAXT, MINT and RH
  • X1 to X3) lagged by 2 weeks
  • Model for December 3rd week
  • Y 80.25 40.25 cos (2.70 X12 - 14.82)
  • 35.78 cos (6.81 X22 8.03)

16
Aphid popn. in 3rd week of December at
Pantnagar
17
GDD approach
  • GDD ? (mean temperature base temperature)
  • The decision of
  • Base temperature
  • Initial time
  • Not much work on base temperature for various
    diseases
  • Normally base temperature is taken as 50 C
  • Under Indian conditions, mean temperature is
    seldom below 50 C
  • Use of GDD and simple accumulation of mean
    temperature will provide similar results in
    statistical models
  • Need for work on base temperature and initial
    time of calculation

18
  • Under Indian conditions, other variables also
    important
  • Model using simple accumulations not found
    appropriate
  • Models based on weighted weather indices

where
19
Y variable to forecast xiw value of
i?th weather variable in w?th period riw
weight given to i-th weather variable in w?th
period riiw weight given to product of xi and
xi in w?th period p number of weather
variables n1 and n2 are the initial and final
periods for which weather variables are
to be included in the model e error term
20
  • Experience based weights
  • Subjective weights based on experience.
  • Weather variable not favourable weight 0
  • Weather variable favourable weight ½
  • Weather variable very favourable weight 1

21
  • Example
  • Favourable relative humidity ? 92
  • Most favourable relative humidity ? 98
  • Weather data
  • Year Week No.
  • 1 2 3 4 5
    6
  • 1993 88.7 90.1 94.4 98.3 98.0
    95.0
  • 94.0 93.3 94.9 93.3 92.0
    88.1
  • 90.3 91.9 90.4 87.9 86.4
    89.7
  • --------------------------------------------------
    --------------
  • --------------------------------------------------
    --------------

22
  • Weighted Index
  • 0x 88.7 0x90.1 0.5 x 94.4 1 x 98.3
  • 1 x 98 0.5 x 95 271.0
  • 0.5 x 94 0.5 x 93.3 0.5 x 94.9
  • 0.5 x 93.3 0.5 x 92 0 x 88.1
    232.6
  • 0 x 90.3 0 x 91.9 0 x 90.4 0x 87.9
  • 0 x 86.4 0 x 89.7 0.0
  • -------------------------------------------------
    --------------
  • --------------------------------------------------
    --------------

23
Interaction Both variables not favourable
weight 0 One variable not favourable, one
variable favourable weight 1/8 One variable
not favourable, one variable highly favourable
weight ¼ Both variables favourable weight
½ One variable favourable, one variable
highly favourable weight ¾ Both variables
highly favourable weight 1
24
Correlation based weights riw
correlation coefficient between Y and i-th
weather variable in w?th period riiw
correlation coefficient between Y and product
of xi and xi in w?th period
25
  • Modified model
  • Model using both weighted and unweighted indices

where
26
  • For each weather variable two types of indices
    have been developed
  • Simple total of values of weather variable in
    different periods
  • Weighted total, weights being correlation
    coefficients between variable to forecast and
    weather variable in respective periods
  • The first index represents total amount of
    weather variable received by the crop during the
    period under consideration
  • The other one takes care of distribution of
    weather variable with reference to its
    importance in different periods in relation to
    variable to forecast
  • On similar lines, composite indices were computed
    with products of weather variables (taken two at
    a time) for joint effects.

27
Pigeon pea
  • Phytophthora blight (Kanpur)
  • Average percent incidence of phytophthora blight
    at one point of time
  • Data used 1985-86 to 1999-2000 on MAXT, MINT,
    RH1, RH2 and RF (X1- X5) from 28th to 33rd SMW
  • Y 330.77 0.12 Z121 .. (R2 0.77)

28
  • Sterility Mosaic
  • Average percent incidence of sterility mosaic
  • Data used 1983-84 to 1999-2000 for MAXT, MINT,
    RH1, RH2 and RF (X1- X5) from 20th to 32nd SMW
  • Y -180.41 0.09 Z121 (R2 0.84)

29
  • Validation for subsequent years

30
(No Transcript)
31
Groundnut
  • Late Leaf Spot Rust Tirupathi
  • Disease indices at one point of time
  • Data used MAXT, MINT, RH1, RH2, RF and WS from
    (X1- X6)
  • - 10th to 14th SMW (Rabi or post rainy)
  • - 41st to 46th SMW (Kharif or rainy)

32
Models for LSS and Rust Disease Index -
groundnut (Tirupati)
Disease Data used Model R2
LLS Kharif 1990 - 1998 Y 39.40 - 0.00921 Z120 0.00037 Z460 0.0022 Z141 0.84
LLS Rabi 1990 - 1999 Y 15.95 0.12Z151 0.0057 Z350 0.83
Rust Kharif 1990 - 1995 Y 0.4213 0.0167Z231 - 0.147 Z10 0.94
33
(No Transcript)
34
  • Principal component regression
  • Independent variables large and correlated
  • Independent variables transformed to principal
    components
  • First few principal components explaining
    desired variation selected
  • Regression model using principal components as
    regressors

35
  • Discriminant function analysis
  • Based on disease status years grouped into
    different categories low, medium, high
  • Linear / quadratic discriminant function using
    weather data in above categories
  • Discriminant score of weather for each year
  • Regression model using disease data as dependent
    variable and discriminant scores of weather as
    independent.
  • Data requirement is more.
  • Can also be used if disease data are qualitative
  • Johnson et al. (1996) used discriminant analysis
    for forecasting potato late blight.

36
  • Deviation method
  • Useful when only 5-6 year data available for
    different periods
  • Week-wise data not adequate for modeling
  • Combined model considering complete data.
  • Not used for disease forewarning but in pest
    forewarning

37
  • Assumption pest population / disease incidence
    in particular year at a given point of time
    composed of two components.
  • Natural growth pattern
  • Weather fluctuations
  • Natural pattern to be identified using data in
    different periods averaged over years.
  • Deviation of individual years in different
    periods from predicted natural pattern to be
    related with deviations of weather.

38
  • Mango
  • Mango fruitfly Lucknow (weekly models)
  • Data used 1993-94 to 1998-99 on MAXT, MINT and
    RH X1 to X3
  • Model for natural pattern

t Week no. Yt Fruitfly population count
at week t
39
(No Transcript)
40
Forecast model
  • Y ? 125.766 0.665 (Y2) 0.115 (1/X222 )
    10.658 (X212)
  • 0.0013 (Y23) 31.788 (1/Y3) ? 21.317
    (X12)
  • ? 2.149 (1/X233) ? 1.746 (1/X234)
  • Y Deviation of fruitfly population from
    natural cycle
  • Yi Fruitfly population in i-th lag week
  • Xij Deviation from average of i-th weather
    variable (i
  • 1,2,3 corresponds to maximum
    temperature,
  • minimum temperature and relative
    humidity) in j-th lag
  • week.

41
Soft Computing Techniques
42
  • With the development of computer hardware and
    software and the rapid computerization of
    business, huge amount of data have been collected
    and stored in centralized or distributed
    databases
  • Data is heterogeneous (mixture of text, symbolic,
    numeric, texture, image), huge (both in
    dimension and size) and scattered.
  • The rate at which such data is stored is growing
    at a phenomenal rate.
  • As a result, traditional statistical techniques
    and data management tools are no longer adequate
    for analyzing this vast collection of data.

43
  • One of the applications of Information Technology
    that has drawn the attention of researchers is
    data mining, where pattern recognition, image
    processing, machine intelligence i.e concerned
    with the development of algorithms and techniques
    that allow system to "learn are directly related
  • Data Mining involves
  • Statistics Provides the background for the
    algorithms.
  • Artificial Intelligence Provides the required
    heuristics for learning the system
  • Data Management Provides the platform for
    storage retrieval of raw and summary data.

44
  • Pattern Recognition and Machine Learning
    principles applied to a very large (both in size
    and dimension) heterogeneous database for
    Knowledge Discovery
  • Knowledge Discovery is the process of identifying
    valid, novel, potentially useful and ultimately
    understandable patterns in data. Patterns may
    embrace associations, correlations, trends,
    anomalies, statistically significant structures
    etc.
  • Without Soft Computing Machine Intelligence and
    Data Mining may remains Incomplete

45
Soft Computing
  • Soft Computing is a new multidisciplinary field
    that was proposed by Dr. Lotfi Zadeh, whose goal
    was to construct new generation Artificial
    Intelligence, known as Computational
    Intelligence.
  • The concept of Soft Computing has evolved. Dr.
    Zadeh defined Soft Computing in its latest
    incarnation as the fusion of the fields of fuzzy
    logic, neural network, neuro-computing,
    Evolutionary Genetic Computing and
    Probabilistic Computing into one
    multidisciplinary system.
  • Soft Computing is the fusion of methodologies
    that were designed to model and enable solutions
    to real world problems, which are not modeled, or
    too difficult to model. These problems are
    typically associated with fuzzy, complex, and
    dynamical systems, with uncertain parameters.
  • These systems are the ones that model the real
    world and are of most interest to the modern
    science.

46
  • The main goal of Soft Computing is to develop
    intelligent system and to solve nonlinear and
    mathematically unmodelled system problems Zadeh
    1993, 1996, and 1999.
  • The applications of Soft Computing have two main
    advantages.
  • First, it made solving nonlinear problems, in
    which mathematical models are not available,
    possible.
  • Second, it introduced the human knowledge such as
    cognition, recognition, understanding, learning,
    and others into the fields of computing.
  • This resulted in the possibility of constructing
    intelligent systems such as autonomous
    self-tuning systems, and automated designed
    systems.

47
soft computing tools
  • Soft computing tools include
  • Fuzzy sets
  • Fuzzy sets provide a natural frame work for the
    process in dealing with uncertainty
  • Artificial neural networks
  • Neural networks are widely used for modelling
    complex functions and provide learning and
    generalization capabilities
  • Genetic algorithms
  • Genetic algorithms are an efficient search and
    optimization tool
  • Rough set theory
  • Rough sets help in granular computation and
    knowledge discovery

48
  • Why Neural Networks are desirable
  • Human brain can generalize from abstract
  • Recognize patterns in the presence of noise
  • Recall memories
  • Make decisions for current problems based on
    prior experience
  • Why Desirable in Statistics
  • Prediction of future events based on past
    experience
  • Able to classify patterns in memory
  • Predict latent variables that are not easily
    measured
  • Non-linear regression problems

49
Application of ANNs
  • Modelling and Control
  • control systems
  • system identification
  • composing music
  • Forecasting
  • economic indicators
  • energy requirements
  • medical outcomes
  • crop forecasts
  • environmental risks
  • Classification
  • medical diagnosis
  • signature verification
  • character recognition
  • voice recognition
  • image recognition
  • face recognition
  • loan risk evaluation
  • data mining

50
  • Neural networks are being successfully applied
    across an extraordinary range of problem domains,
    in areas as diverse as finance, medicine,
    engineering, geology, biology, physics and
    agriculture.
  • From a statistical perspective neural networks
    are interesting because of their potential use in
    prediction and classification problems.
  • A very important feature of these networks is
    their adaptive nature, where Learning by
    Example replaces Programming in solving
    problems.
  • Basic capability of neural networks is to learn
    patterns from examples

51
  • Type of neural network models
  • Two types of neural network models
  • Multilayer perceptron (MLP) with different hidden
    layers and nodes
  • Radial basis function (RBF)

52
Neural network based model
  • Steps in developing a neural network model
  • Forming training, testing and validation sets
  • Neural network model
  • No. of input nodes
  • No. of hidden layers
  • No. of hidden nodes
  • No. of output nodes
  • Activation function
  • Model building
  • Sensitivity Analysis

53
Data sets
  • The data available is divided into three data
    sets
  • Training set represents the input- output
    mapping, which is used to modify the weights.
  • Validation set is required only to decide when to
    stop training the network, and not for weight
    update.
  • Test set is the part of collected data that is
    set aside to test how well a trained neural
    network generalizes.

54
  • No. of input nodes more than one
  • No. of hidden layers one / two
  • No. of hidden nodes decided by various rules
  • No. of output nodes one
  • Activation function hyperbolic

55
  • Activation function
  • Activation functions determine the output of a
    processing node. Non linear functions have been
    used as activation functions such as logistic,
    tanh etc.
  • Activation functions such as sigmoid are commonly
    used because they are nonlinear and continuously
    differentiable which are desirable for network
    learning
  • Logistic activation functions are mainly used for
    classification problems which involve learning
    about average behavior
  • Hyperbolic tangent functions are used for the
    problem involves learning about deviations from
    the average such as the forecasting problem.
  • Therefore, in the present study, hyperbolic
    tangent (tanh) function has been used as
    activation function for neural networks model
    based on MLP architecture.

56
Input
Output
57
Learning of ANNs
  • The most significant property of a neural network
    is that it can learn from environment, and can
    improve its performance through learning
  • Learning is the process of modifying the weights
    in networks
  • The network becomes more knowledgeable about
    environment after each iteration of learning
    process.
  • There are mainly two types of learning paradigms
  • Supervised learning
  • Unsupervised learning

58
A learning cycle in the MLP (Backpropagation
Learning Algorithm)
59
Three-layer back-propagation neural network
60
  • Mustard
  • Alternaria blight (Varuna, Rohini Binoy)
  • Bharatpur (Raj)
  • Behrampur (WB)
  • Dholi (Bihar)
  • Powdery mildew (Varuna and GM2)
  • S.K.Nagar
  • Variable to forewarn
  • crop age at first appearance of disease
  • crop age at peak severity of disease
  • maximum severity of disease
  • Cotton
  • Bacterial blight ( of disease incidence) - Akola

61
Pests / diseases forewarning-Mustard
  • Data have been taken from Mission Mode Project
    under National Agricultural Technology Project,
    entitled Development of weather based
    forewarning system for crop pests and diseases,
    at CRIDA, Hyderabad.
  • Models were developed for forecasting different
    aspects relating to diseases for Alternaria
    Blight (AB) and Powdery Mildew (PM) in Mustard
    crop.
  • The field trials were sown on 10 dates at weekly
    intervals (01, 08, 15, 22, 29 October, 05, 12,
    19, 26 November and 03 December) at each of the
    locations viz., Bharatpur, Dholi and Berhampur
    for Alternaria Blight and at S.K.Nagar for
    Powdery Mildew.
  • Data for different dates of sowing were taken
    together for model development.
  • Weekly data on weather variables starting from
    week of sowing up to six weeks of crop growth
    were considered
  • Forewarning models were developed for two
    varieties of mustard crop for
  • Alternaria Blight on leaf and pod (Varuna and
    Rohini Bharatpur, Varuna and Binoy Behrampur
    and Varuna and Pusabold Dholi) and
  • Powdery Mildew on leaf (Varuna and GM2
    S.K.Nagar)
  • Models have been validated using data on
    subsequent years not included in developing the
    models.

62
Mean Absolute Percentage Error of various models
at Bharatpur in different varieties in mustard
crop for Alternaria blight (AB) - 2006-07
Character Variety MLP RBF WI
Maximum severity Varuna (on Leaf) 111.0 153.8 150.1
Age at First app Varuna (on Leaf) 14.0 15.1 14.7
Age at Peak Severity Varuna (on Leaf) 14.1 27.3 22.3
Maximum severity Varuna (on Pod) 113.7 143.6 132.6
Age at First app Varuna (on Pod) 15.7 9.2 14.2
Age at Peak Severity Varuna (on Pod) 3.9 6.4 5.4
Maximum severity Rohini (on Leaf) 184.0 200.6 196.3
Age at First app Rohini (on Leaf) 12.0 15.5 8.9
Age at Peak Severity Rohini (on Leaf) 28.3 27.8 26.2
Maximum severity Rohini (on Pod) 174.8 220.4 229.6
Age at First app Rohini (on Pod) 29.3 28.2 24.7
Age at Peak Severity Rohini (on Pod) 17.2 20.7 19.6
63
  • Neural networks, with their remarkable ability to
    derive meaning from complicated or imprecise
    data, can be used to extract patterns and
    classifications
  • Neural networks do not perform miracles. But if
    used sensibly they can produce some amazing
    results

64
  • Model for qualitative data
  • Data in categories
  • Occurrence / non-occurrence, low / medium / high,
    etc.
  • Classified as 0 / 1 (2 categories) 0,1,2
    (three categories)
  • Quantitative data / mixed data can be converted
    to categories

65
Logistic Regression model
  • where, L ß0 ß1x1 ß2x2 .ßnxn
  • x1 , x2 , x3 ,xn are weather variables/weather
    indices
  • e random error
  • Forecast / Prediction rule
  • If P lt 0.5, then the probability of epidemic
    occurrence will be minimal
  • If P ? 0.5, then there is more chance of
    occurrence of epidemic.

66
  • Rice
  • Leaf blast severity () - Palampur at one point
    of time
  • Data used 1991-92 to 1998-99 on MAXT, MINT, RH1,
    RH2, BSH RF X1 to X6 from 23th to 31st
    SMW.
  • Model
  • L 394.8 -0.0520 Z351-1.5414 Z10
  • Validation for subsequent years

Year Observed Forewarning Probabilities
1999-00 1 1 0.88
2000-01 1 1 0.63
67
Mustard
  • Alternaria blight and White rust
  • Data used 1987-88 to 1998-99 on MAXT, MINT, RH1,
    RH2 and BSH (X1 to X5) from week of sowing (n1)
    to 50th smw (n2)
  • Model for Alternaria blight
  • L - 8.8347 0.0163 Z120 - 0.00037 Z130 -
    0.00472 Z450
  • Model for White rust
  • L 5.8570 - 0.0293Z40 0.00264 Z230
  • Forecasts of subsequent years are

Alternaria blight Alternaria blight Alternaria blight Alternaria blight White Rust White Rust White Rust
Year Observed Forewarning Prob. Observed Forewarning Prob.
1999-00 1 1 0.51 1 1 0.96
2000-01 0 0 0.13 0 0 0.49
2001-02 1 1 0.62 0 0 0.37
68
  • Within year model
  • Model using only one years data
  • Data availability for several dates of sowing
  • If adequate dates of sowing, models similar to
    between-year models could be developed
  • Use for forewarning subsequent years (?)
  • Model for single date of sowing
  • Forewarning of maximum disease severity
  • Applicable when 10-12 data observations between
    first disease appearance and maximum disease
    severity
  • Non-linear model for disease development pattern
    growth using partial data

69
  • Mustard
  • Alternaria blight cv. Varuna ( disease severity)
    - Kumarganj
  • Data used 1999-2000
  • Model
  • Yt A exp (B/t)
  • Yt pds at time t, A and B are
    parameters,
  • t week after sowing (1,2,.)

70
Observed, predicted and forecasts of max. percent
disease severity (PDS)
  • Reliable forecast of max. pds could be obtained
    for 2 weeks in advance

71
Models developed at IASRI
  • Sugarcane
  • Pyrilla
  • Early shoot borer
  • Top borer
  • Pigeon pea
  • Pod fly
  • Pod borer
  • Sterility Mosaic
  • Phytophthora Blight
  • Rice
  • BPH
  • Gall midge
  • Mango
  • Powdery Mildew
  • hoppers
  • fruit-fly
  • Mustard
  • Alternaria Blight
  • White Rust
  • Powdery Mildew
  • Aphid
  • Cotton
  • American boll worm
  • Pink boll worm
  • Spotted boll worm
  • Whitefly
  • Groundnut
  • Spodoptera litura
  • Late leaf blast
  • Rust
  • Onion
  • Thrips

72
References
  • Agrawal, Ranjana, Jain, R.C. and Jha, M.P.
    (1983). Joint effects of weather variables on
    rice yields. Mausam, 34(2), 177-81.
  • Agrawal, Ranjana, Jain, R.C., Jha, M.P., (1986).
    Models for studying rice crop weather
    relationship, Mausam, 37(1), 67-70.
  • Agrawal Ranjana, Mehta, S.C., Kumar, Amrender and
    Bhar, L.M. (2004). Development of weather based
    forewarning system for crop pests and diseases-
    Report from IASRI, Mission mode project under
    NATP, PI, Dr. Y.S. Ramakrishna, CRIDA,
    Hyderabad.
  • Denton, J.W., 1995. How good are neural networks
    for causal forecasting? Journal of Business
    Forecasting, 14 (2), 1720.
  • Desai, A.G., Chattopadhyay, C., Agrawal, Ranjana,
    Kumar, A., Meena, R.L., Meena, P.D., Sharma,
    K.C., Rao, M. Srinivasa, Prasad,, Y.G. and
    Ramakrishna, Y.S. (2004). Brassica juncea
    powdery mildew epidemiology and weather-based
    forecasting models for India - a case study ,
    Journal of Plant Diseases and Protection, 111(5),
    429-438.
  • Gaudart, J., Giusiano, B. and Huiart, L. (2004).
    Comparison of the performance of multi-layer
    perceptron and linear regression for
    epidemiological data. Comput. Statist. Data
    Anal., 44, 547-70.

73
  • Hebb, D.O. (1949) The organization of behaviour
    A Neuropsychological Theory, Wiley, New York.
  • Hopfield, J.J. (1982). Neural network and
    physical system with emergent collective
    computational capabilities. In proceeding of the
    National Academy of Science (USA) ,79, 2554-2558.
  • Kaastra, I. and Boyd, M.(1996) Designing a
    neural network for forecasting financial and
    economic time series. Neurocomputing, 10(3),
    215-236.
  • Masters, T. (1993). Practical Neural Network
    Recipes in C, San Diego, Academic Press.
  • Rosenblatt, F. (1958). The perceptron A
    probabilistic model for information storage and
    organization in the brain. Psychological review,
    65, 386-408.
  • Rumelhart, D.E., Hinton, G.E., and Williams, R.J.
    (1986). Learning internal representations by
    error propagation, Nature, 323, 533-536

74
  • Saanzogni, Louis and Kerr, Don (2001) Milk
    production estimate using feed forward artificial
    neural networks. Computer and Electronics in
    Agriculture, 32, 21-30.
  • Warner, B. and Misra, M. (1996). Understanding
    neural networks as statistical tools. American
    Statistician, 50, 284-93.
  • Widrow, B. and Hoff, M.E. (1960). Adaptive
    switching circuit. IREWESCON convention record,
    4, 96-104
  • Zhang, G., Patuwo, B. E. and Hu, M. Y. (1998).
    Forecasting with artificial neural networks The
    state of the art. International Journal of
    Forecasting, 14, 35-62.

75
Thank You
About PowerShow.com