Pests and Diseases Forewarning System. Amrender Kumar. Scientist Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, INDIA email@example.com. Crop – Pests - Weather Relationship. Crop. Weather. Pests.
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Indian Agricultural Statistics Research Institute,
Library Avenue, New Delhi, INDIA
Historical data at periodical intervals for 10-15 years
A day is potato late blight favorable if
Trivedi et al. (1999)
Y = 0 + 1 X1+2 X2 ………. +p Xp + e ,
Y= 133.18 - 3.09 RH2L4 + 1.68 RFL4 (R2=0.78)
– [X1 to X3) lagged by 2 weeks
Y = 80.25 + 40.25 cos (2.70 X12 - 14.82)
+ 35.78 cos (6.81 X22 + 8.03)
The decision of
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 calculationGDD approach
xiw value of ith weather variable in wth period
riw weight given to i-th weather variable in wth period
rii’w weight given to product of xi and xi’ in wth 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
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
riw correlation coefficient between Y and i-th weather
variable in wth period
rii’w correlation coefficient between Y and product of xi and xi’
in wth period
Phytophthora blight (Kanpur)
Y = 330.77 + 0.12 Z121 ….. (R2 = 0.77)
Y = -180.41 + 0.09 Z121 …… (R2 = 0.84)
Late Leaf Spot & Rust – Tirupathi
- 10th to 14th SMW (Rabi or post rainy)
- 41st to 46th SMW (Kharif or rainy)
t = Week no.
Yt = Fruitfly population count at week t
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
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.
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
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
Soft Computing is a new multidisciplinary field that was proposed by Dr.LotfiZadeh, 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.
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.
Soft computing tools include
Human brain can generalize from abstract
Recognize patterns in the presence of noise
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
loan risk evaluation
Modelling and Control
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
Two types of neural network models
Multilayer perceptron (MLP) with different hidden layers and nodes
Radial basis function (RBF)
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
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.
No. of hidden layers : one / two
No. of hidden nodes : decided by various rules
No. of output nodes : one
Activation function : hyperbolic
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.
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
Alternaria blight (Varuna, Rohini & Binoy)
Powdery mildew (Varuna and GM2)
Variable to forewarn
crop age at first appearance of disease
crop age at peak severity of disease
maximum severity of disease
Bacterial blight (% of disease incidence) - Akola
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.
Mean Absolute Percentage Error of various models at Bharatpur in different varieties in mustard crop for Alternaria blight (AB) - 2006-07
Neural networks do not perform miracles. But if used sensibly they can produce some amazing results
L= 394.8 -0.0520 Z351-1.5414 Z10
Alternaria blight and White rust
Model forAlternaria 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
Observed, predicted and forecasts of max. percent disease severity (PDS)