A GRICULTURE: A Field for Development using AI Techniques

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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CS621-Artificial Intelligence Course Seminar

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


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


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


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


Hortibot robot for weeding


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Source: http://www.lovingthemachine.com/2008/04/farmer-hails-weeding.html


Displacement of a Robot


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• Currently, Research on “Agricultural robots” is active in Japan and Korea


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.


Thank You !


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Questions??



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Photosynthesis pathways of Benson-calvin cycle

Photo respiration



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A GRICULTURE: A Field for Development using AI Techniques

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