This document discusses using artificial intelligence techniques to develop applications for agriculture. It identifies several areas where AI could enhance the agriculture sector, including monitoring crop conditions, weather, ecosystems and decision support. Specific applications discussed include intelligent environment control for plant production, intelligent robots in agriculture, and using computational intelligence and algorithms like genetic algorithms and neural networks to optimize bio-systems and fit quantitative models. The document provides examples of using these techniques for problems like nutrient control for plant growth and maximizing fruit yield by balancing vegetative and reproductive plant growth.
2. Outline
Talk 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
2
4. 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
4
5. 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
Efficiency
curves
5
Philosophy
of
Efficiency
Different Technologies
6. Why to choose “Field of Agriculture”?
• Peak in the agricultural sector will again reach in near future
6
7. 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.
7
14. 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
14
17. 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.
17
18. 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}
18
19. Optimization Problem
• Objective Function :
• Objective Problem
Maximize F(NC)
Subject to α1 ≤ NC(k) ≤ α2
N
N
K
L L
K
SD
K
TLL
N
N
NC
F
1
3 3
)
(
)
(
1
1
)
(
19
22. 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
22
25. Hortibot robot for weeding
25
Source: http://www.lovingthemachine.com/2008/04/farmer-hails-weeding.html
26. Displacement of a Robot
26
• Currently, Research on “Agricultural robots” is active in Japan and
Korea
27. 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
27
28. References:
28
[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.
A man producing an industrial item can sell it anywhere in the country but the same freedom is not given to the farmer.
we should remove unnecessary restrictions — movement, stocking, and so on. Give the farmer a good, remunerative price, which is better than the various sops being offered.
