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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 1 CS621-Artificial Intelligence Course Seminar

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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 CS621-Artificial Intelligence Course Seminar

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Part-I 3 CS621-Artificial Intelligence Course Seminar

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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 4 CS621-Artificial Intelligence Course Seminar

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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 5 CS621-Artificial Intelligence Course Seminar

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Why to choose “Field of Agriculture”? Peak in the agricultural sector will again reach in near future CS621-Artificial Intelligence Course Seminar 6

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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. 7 CS621-Artificial Intelligence Course Seminar

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Computational Intelligence in Agriculture and the Environment CS621-Artificial Intelligence Course Seminar 8

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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) CS621-Artificial Intelligence Course Seminar 9

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Photo-Synthetic Algorithm CS621-Artificial Intelligence Course Seminar 10 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 CS621-Artificial Intelligence Course Seminar 11 Example: In Part-II, Nutrient control set for plant growth has been solved by PS Algorithm

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Part-II 12 CS621-Artificial Intelligence Course Seminar

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Intelligent Environment Control For Plant Production System 13 CS621-Artificial Intelligence Course Seminar

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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 CS621-Artificial Intelligence Course Seminar 14

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Hydroponic System 15 CS621-Artificial Intelligence Course Seminar

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16 CS621-Artificial Intelligence Course Seminar

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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. 17 CS621-Artificial Intelligence Course Seminar

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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} 18 CS621-Artificial Intelligence Course Seminar

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Optimization Problem Objective Function : Objective Problem Maximize F(NC) Subject to α1 ≤ NC(k) ≤ α2 19 CS621-Artificial Intelligence Course Seminar

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Neural Networks 20 CS621-Artificial Intelligence Course Seminar

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Genetic Algorithm 21 CS621-Artificial Intelligence Course Seminar

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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 CS621-Artificial Intelligence Course Seminar 22

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Intelligent Robots in Agriculture CS621-Artificial Intelligence Course Seminar 23

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CS621-Artificial Intelligence Course Seminar 24 Strawberry harvesting robot Source: http://www.lovingthemachine.com/2008/04/farmer-hails-strawberry-picking-robot.html

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Hortibot robot for weeding CS621-Artificial Intelligence Course Seminar 25 Source: http://www.lovingthemachine.com/2008/04/farmer-hails-weeding.html

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Displacement of a Robot CS621-Artificial Intelligence Course Seminar 26 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 27 CS621-Artificial Intelligence Course Seminar

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References: 28 CS621-Artificial Intelligence Course Seminar [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.

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Thank You ! CS621-Artificial Intelligence Course Seminar 29 Questions??

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CS621-Artificial Intelligence Course Seminar 30 Photosynthesis pathways of Benson-calvin cycle Photo respiration

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CS621-Artificial Intelligence Course Seminar 31

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