a griculture: a field for development using ai techniques
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A GRICULTURE: A Field for Development using AI Techniques. - Lets Identify the Applications. Presented by: V S K Murthy B (08407403) Singre Pawan (07305039). CS621 Course Tutor: Prof. Pushpak Bhattacharya. Talk is divided into two parts: Part-I: - PowerPoint PPT PresentationTRANSCRIPT
AGRICULTURE: A Field for Development using AI Techniques
- Lets Identify the Applications
Presented by:V S K Murthy B (08407403)Singre Pawan (07305039)
CS621 Course Tutor:
Prof. Pushpak Bhattacharya
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V S K Murthy and Singre Pavan
OutlineTalk is divided into two parts:• Part-I:
▫Why to choose “field of Agriculture” ?▫ Identified Areas for enhancing Agriculture sector ▫Computational Intelligence in Agriculture and Environment
• Part-II:▫ Intelligent Environment Control for Plant Production▫ Intelligent Robot in Agriculture
• Conclusion
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Part-I
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Why to choose “Field of Agriculture”? • Sector status in India
▫ Growth of socio-economic sector in India ▫ Means of living for almost 66% of the employed class in India▫ Acquired 18% of India's GDP▫ Occupied almost 43% of India's geographical area
• Huge investment made for Irrigation facilities etc. in 11th five year plan
• Introduction of de-regulation in agriculture sector▫ Opens competition for agriculture products▫ Removal of unnecessary restrictions — movement, stocking, and
so on.. ▫ Good price to farmer ▫ Substantial technology growth in coming years
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Why to choose “Field of Agriculture”?
• Any process growth rates can be linked with efficiency curves• Due to deregulation, Agriculture has bright future insight
Time scale
Eff
icie
ncy
cu
rves
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Philosophy of
Efficiency Different Technologies
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Why to choose “Field of Agriculture”? • Peak in the agricultural sector will again reach in near future
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Identified Areas for enhancing Agriculture sector • Needs monitoring on
▫ Agricultural crop conditions▫ Weather and climate▫ Ecosystems
• Decision support for agricultural planning and policy-making• On the basis of AI interest
▫ Computational Intelligence in Agriculture and the Environment Optimizing different types of bio-systems Testing and fitting of quantitative models
▫ Intelligent environment control for plant production systems▫ Intelligent robots in agriculture▫ An expert geographical information system for land evaluation▫ Artificial neural network for plant classification using image processing.▫ Control of green house.
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Computational Intelligence in Agriculture and the Environment
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• Search procedures ▫ Exhaustive techniques (random walk)▫ Calculus based methods (gradient methods) ▫ Partial knowledge techniques (hill climbing)▫ Knowledge based techniques (Production rule systems, heuristic
methods)▫ Stochastic techniques (SA)▫ Biologically inspired algorithms (GA and immune)
• Problems deal with optimizing bio-systems and fitting quantitative models require ▫ Refinement or processing using adaptive search procedures
• Bio-system Derived Algorithms (BDAs)▫ Photosynthetic Algorithm (PA)▫ Leaf Cellular Automate (LCA)
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Photo-Synthetic Algorithm
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DHAP(Knowledge
string)
DHAP(Knowledge
string)
Photo-Respiration
Benson-CalvinCycle
Light (Stimulation
)
Oxygen/CO2
concentration
CO2 Reservoir
GAP
FitnessFitness
RuBP
Discard
Next Iteration
Poor
Copy G
ood
Atmosphere
o Any problem that can be solved by GA can also be solves by PS Algorithm
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Similarities of GA and PA Algorithms
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Example: In Part-II, Nutrient control set for plant growth has been solved by PS Algorithm
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Part-II
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Intelligent Environment Control For Plant Production System
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Why it is required?
• To increase productivity of crops• Care for special herbal valued plants, environment diverse
plants etc., which in turn increases our export value• To develop decision making support
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Hydroponic System
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Plant Growth Optimization Problem
• In plant production, good fruit yield requires an optimal balance between ▫ Vegetative growth (e.g. root, stem, leaf growth) ▫ Reproductive growth (e.g. flower and fruit growth)
• NNs and GA provides optimal set points of the nutrient concentration (NC).
• The ratio of total leaf length (TLL) to stem diameter (SD) defines as a predictor for plant production growth.
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Optimization Problem• Let TLL(k)/SD(k) be the time series of TLL/SD as affected by NC(k)
(k=1,......,N; N : final day)• Seedling stage(1 ≤ k ≤ N ) divided into four steps:
▫ Transplanting ▫ Vegetative growth after transplanting▫ Flowering of the first truss▫ Fruit setting for the first truss and flowering for the
second truss.• Consider the value of nutrient concentration at each step is NC1,
NC2, NC3, NC4 . {1≤ k ≤ N1L : step1, N1L+1 ≤ k ≤ N2L: step2,
N2L+1 ≤ k ≤ N3L : step3, N3L+1 ≤ k ≤ N : step4}
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Optimization Problem
• Objective Function :
• Objective Problem Maximize F(NC) Subject to α1 ≤ NC(k) ≤ α2
N
NKL LKSD
KTLL
NNNCF
13 3)(
)(
1
1)(
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Neural Networks
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Genetic Algorithm
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Procedure of GA• Step1: The Initial population consisting of several individuals • Step2 : Several individuals in another population are added to
original population to maintain diversity• Step3 : Crossover and mutation operations are applied to the
individuals• Step4 : Fitness values of all individuals are calculated by NN
model• Step5 : Superior individuals are selected and retained for next
generation• Step6 : step 2 through 5 are repeated until an arbitrary
condition satisfied
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Intelligent Robots in Agriculture
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Strawberry harvesting robot
Source: http://www.lovingthemachine.com/2008/04/farmer-hails-strawberry-picking-robot.html
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Hortibot robot for weeding
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Source: http://www.lovingthemachine.com/2008/04/farmer-hails-weeding.html
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Displacement of a Robot
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• Currently, Research on “Agricultural robots” is active in Japan and Korea
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Conclusion• Need for AI focus on Agriculture sector is discussed• Bio-system Derived Algorithms (BDAs) are explored• Identified intelligent approaches which are useful for
mechanizing complex agricultural systems• Growing Research and technology should contribute to the
basic amenities in agriculture
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References:
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[1] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison Wesley, 1989.
[2] J.H. Holland, “Genetic algorithms,” Sci. Amer., pp. 44-50, July 1992.
[3] J.B. Bowyer and R.C. Leegood, “Photosynthesis,” in Plant Biochemistry, P.M. Dey and J.B. Harborne, Eds. San Diego, CA: Academic, 1997, pp. 49-110.
[4] N. Kawamura, K. Namikawa, T. Fujiura, and M. Ura, “Study on agricultural robot,” J. Jpn. Soc. Agricultural Mach., vol. 46, no. 3, pp. 353-358, 1984.
[5] Y. Hashimoto and K. Hatou, “Knowledge based computer integrated plant factory,” inProc. 4th Int. Cong. Computer Technology in Agriculture, 1992, pp. 9-12.
[6] Y. Hashimoto, “Applications of artificial neural networks and genetic algorithms to agricultural systems,” Comput. Electron. Agriculture, vol. 18, no. 2,3, pp. 71-72, 1997.
[7] Yasushi Hashimoto, Haruhiko murase, “Intelligent systems for agriculture in japan”. IEEE Control systems Magazine, Oct 2001.
V S K Murthy and Singre Pavan
Thank You !
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Questions??
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Photosynthesis pathways of Benson-calvin cycle
Photo respiration
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