1. Pests and Diseases Forewarning System
Amrender Kumar
Scientist
Indian Agricultural Statistics Research Institute,
Library Avenue, New Delhi, INDIA

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
Observation 1 2 3 4 .
y11
y12
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 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 year’s last population of pathogen

10. Forecast Models Between year models Within 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 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 < C
the total rainfall for the last 10 days is > 3.0 cm
the minimum temperature on that day is > 7.20 C
Trivedi et al. (1999)

12. Relationship between two or more quantitative variables
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
i’s are regression coefficients
Xi’s 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: on MAXTemp, MINTemp, RH1 (morn), RH2 (aft) and RF – [X1 to X5) lagged by 2 to 4 weeks
Model for 44th SMW
Y= RH2L RFL4 (R2=0.78)

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: on MAXT, MINT and RH
– [X1 to X3) lagged by 2 weeks
Model for December 3rd week
Y = cos (2.70 X )
cos (6.81 X )

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
rii’w 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.

22. Weighted Index
0x x x x
1 x x 95 =
0.5 x x x
0.5 x x x =
0 x x x x
0 x x = 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
rii’w correlation coefficient between Y and product of xi and xi’
in wth period

25. Model using both weighted and unweighted indices
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 : to on MAXT, MINT, RH1, RH2 and RF (X1- X5) from 28th to 33rd SMW
Y = Z121 ….. (R2 = 0.77)

28. Sterility Mosaic Average percent incidence of sterility mosaic
Data used : to for MAXT, MINT, RH1, RH2 and RF (X1- X5) from 20th to 32nd SMW
Y = Z121 …… (R2 = 0.84)

29. Validation for subsequent years :

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)
Data used
Model R2
LLS Kharif
Y = Z Z460 +
Z141
0.84
LLS Rabi
Y = Z Z350
0.83
Rust Kharif
Y = Z Z10
0.94

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: to on MAXT, MINT and RH – [X1 to X3]
Model for natural pattern
t = Week no.
Yt = Fruitfly population count at week t

40. Forecast model
Y =  (Y2) (1/X222 ) (X212)
(Y23) (1/Y3)  (X12)
 (1/X233)  (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 Forecasting: 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)
Input vector
Output vector
Target vector
Differences
Adjust weights
ANN model =

59. Three-layer back-propagation neural network

60. Mustard Cotton 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)
Character
Variety
MLP
RBF WI
Maximum severity
Varuna (on Leaf)
111.0
153.8
150.1
Age at First app
14.0
15.1
14.7
Age at Peak Severity
14.1
27.3
22.3
Varuna (on Pod)
113.7
143.6
132.6
15.7
9.2
14.2
3.9
6.4
5.4
Rohini (on Leaf)
184.0
200.6
196.3
12.0
15.5
8.9
28.3
27.8
26.2
Rohini (on Pod)
174.8
220.4
229.6
29.3
28.2
24.7
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= β0+ β1x1+ β2x2 ….βnxn
x1 , x2 , x3 ,…xn are weather variables/weather indices
e = random error
Forecast / Prediction rule
If P < 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: to on MAXT, MINT, RH1, RH2, BSH & RF – [X1 to X6] from 23th to 31st SMW.
Model :
L= Z Z10
Validation for subsequent years :
Year
Observed
Forewarning
Probabilities 1
0.88
0.63

67. Alternaria blight and White rust
Mustard
Alternaria blight and White rust
Data used: to on MAXT, MINT, RH1, RH2 and BSH – (X1 to X5) from week of sowing (n1) to 50th smw (n2)
Model for Alternaria blight
L = Z Z Z450
Model for White rust
L = Z Z230
Forecasts of subsequent years are
Alternaria blight
White Rust
Year
Observed
Forewarning
Prob. 1
0.51
0.96
0.13
0.49
0.62
0.37

68. Within year model Model using only one year’s 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 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:
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
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

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75. Thank You