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AGRICULTURE
A Field for Development using Information
and Communication Technologies
PRESENTED BY:
Baibhav Agrawal
( 2015 MB 11 )
Piyush Agarwal
( 2015 MB 22 )
COURSE TUTOR:
Dr. G. P. Sahu
Talk is divided into two parts:
Part-I:
Importance of Agriculture in india
Steps Already Taken at Government Level
Identified Areas for enhancing Agriculture sector
Part-II:
Computational Intelligence in Agriculture and Environment
Intelligent Environment Control for Plant Production
Intelligent Robot in Agriculture
Conclusion
• Sector status in India
▫ Acquired 14% of India's GDP
▫ Means of living for almost 66% of the employed class in India
▫ Occupied almost 43% of India's geographical area
▫ Opens competition for agriculture products
▫ Removal of unnecessary restrictions — movement, stocking.
▫ Good price to farmer
▫ Substantial technology growth in coming years
Steps Already Taken at Government Level
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 Artificial Intelligence interest
▫ Computational Intelligence in Agriculture and the Environment
▫ 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.
Computational Intelligence in Agriculture and
Environment
• Search Procedures
• Exhaustive techniques (random walk)
• Calculus based methods (gradient methods)
• Partial knowledge techniques (hill climbing)
• Knowledge based techniques (Production rule systems)
• Biologically inspired algorithms (GA and immune) .
• Bio-system Derived Algorithms (BDAs)
• Photosynthetic Algorithm (PA)
• Genetic Algorithm (GA)
Photo-Synthetic Algorithm
DHAP
(Knowledge string)
Photo-
Respiration
Benson-
Calvin
Cycle
Light
(Stimulation)
Oxygen/CO2
concentration
CO2 Reservoir
GAP
Fitness
RuBP
Discard
Next Iteration
Poor
CopyGood
Atmosphere
o Any problem that
can be solved by GA
can also be solved
by PS Algorithm
Intelligent Environment Control For Plant Production
System
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
Hydroponic System
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.
• The ratio of Total Leaf Length to Stem Diameter defines as a predictor
for plant production growth.
Intelligent Robots in Agriculture
Strawberry Harvesting Robot
Displacement of a Robot
Currently, Research on “Agricultural robots” is active in Japan and Korea
Conclusion
• Need for AI focus on Agriculture sector is discussed
• Identified intelligent approaches which are useful for mechanizing
complex agricultural systems
• Growing Research and technology should contribute to the basic
amenities in agriculture
References
[1] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA:
Addison Wesley, 1989.
[2] 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.
[3] 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.
[4] Y. Hashimoto and K. Hatou, “Knowledge based computer integrated plant factory,” inProc. 4th Int.
Cong. Computer Technology in Agriculture, 1992, pp. 9-12.
[5] 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.
[6] Yasushi Hashimoto, Haruhiko murase, “Intelligent systems for agriculture in japan”. IEEE Control
systems Magazine, Oct 2001.
AGRICULTURE FIELD DEVELOPMENT ICT
AGRICULTURE FIELD DEVELOPMENT ICT

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AGRICULTURE FIELD DEVELOPMENT ICT

  • 1. AGRICULTURE A Field for Development using Information and Communication Technologies PRESENTED BY: Baibhav Agrawal ( 2015 MB 11 ) Piyush Agarwal ( 2015 MB 22 ) COURSE TUTOR: Dr. G. P. Sahu
  • 2. Talk is divided into two parts: Part-I: Importance of Agriculture in india Steps Already Taken at Government Level Identified Areas for enhancing Agriculture sector Part-II: Computational Intelligence in Agriculture and Environment Intelligent Environment Control for Plant Production Intelligent Robot in Agriculture Conclusion
  • 3.
  • 4. • Sector status in India ▫ Acquired 14% of India's GDP ▫ Means of living for almost 66% of the employed class in India ▫ Occupied almost 43% of India's geographical area
  • 5. ▫ Opens competition for agriculture products ▫ Removal of unnecessary restrictions — movement, stocking. ▫ Good price to farmer ▫ Substantial technology growth in coming years Steps Already Taken at Government Level
  • 6. 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 Artificial Intelligence interest ▫ Computational Intelligence in Agriculture and the Environment ▫ 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.
  • 7.
  • 8. Computational Intelligence in Agriculture and Environment • Search Procedures • Exhaustive techniques (random walk) • Calculus based methods (gradient methods) • Partial knowledge techniques (hill climbing) • Knowledge based techniques (Production rule systems) • Biologically inspired algorithms (GA and immune) . • Bio-system Derived Algorithms (BDAs) • Photosynthetic Algorithm (PA) • Genetic Algorithm (GA)
  • 9. Photo-Synthetic Algorithm DHAP (Knowledge string) Photo- Respiration Benson- Calvin Cycle Light (Stimulation) Oxygen/CO2 concentration CO2 Reservoir GAP Fitness RuBP Discard Next Iteration Poor CopyGood Atmosphere o Any problem that can be solved by GA can also be solved by PS Algorithm
  • 10. Intelligent Environment Control For Plant Production System 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
  • 12.
  • 13. 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. • The ratio of Total Leaf Length to Stem Diameter defines as a predictor for plant production growth.
  • 14. Intelligent Robots in Agriculture Strawberry Harvesting Robot
  • 15. Displacement of a Robot Currently, Research on “Agricultural robots” is active in Japan and Korea
  • 16. Conclusion • Need for AI focus on Agriculture sector is discussed • Identified intelligent approaches which are useful for mechanizing complex agricultural systems • Growing Research and technology should contribute to the basic amenities in agriculture
  • 17. References [1] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison Wesley, 1989. [2] 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. [3] 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. [4] Y. Hashimoto and K. Hatou, “Knowledge based computer integrated plant factory,” inProc. 4th Int. Cong. Computer Technology in Agriculture, 1992, pp. 9-12. [5] 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. [6] Yasushi Hashimoto, Haruhiko murase, “Intelligent systems for agriculture in japan”. IEEE Control systems Magazine, Oct 2001.