T. Raguchander

Work place: Tamilnadu Agricultural University, Coimbatore, India

E-mail: raguchander@rediffmail.com


Research Interests:


Dr.T.Raguchander is working as Professor in the Department of plant Pathology, Tamilnadu Agricultural University, Coimbatore, 641 003, India. His area of specialization are phytotoxin inactivation by microbes, plant growth promoting rhizobacteria mediated resistance, development of bioformulation for the management of plant disease and Molecular Plant Pathology. He obtained 14 externally funded schemes from several National and International firms. He has published 80 research papers in International journals, 100 research papers in National journals and 4 books. His Citation index is 561 till 2012 and citation index in scopus is 12.5. He is currently interested in interdisciplinary studies to enhance the portability of agricultural studies especially in plant disease management and betterment of farming community by user friendly technology deliverable media.

Author Articles
Rough Set Model for Nutrition Management in Site Specific Rice Growing Areas

By K. Lavanya N.Ch.S.N. Iyengar M.A. Saleem Durai T. Raguchander

DOI: https://doi.org/10.5815/ijisa.2014.10.10, Pub. Date: 8 Sep. 2014

The optimized fertilizer usage for better yield of rice cultivation is influenced by key factors like soil fertility, crop variety, duration, season, nutrient content of the fertilizer, time of application etc., It is observed that 60 percent of yield gap in tamilnadu is due to farmers lack of knowledge on key factors and informal sources of information by pesticide dealers. In this study the major contributing factors for fertilizer requirement and optimum crop yield were analyzed based on rough set theory. In data analytics perspective the nutrient plan is sort of multiple attribute decision-making processes. To reduce the complexity of decision making, key factors that are indiscernible to conclusion are eliminated. Our rough set based approach improved the quality of agricultural data through removal of missing and redundant attributes. After pretreatment the data formed as target information, then attribute reduction algorithm was used to derive rules. The generated rules were used to structure the nutrition management decision-making. The precision was above 88% and experiments proved the feasibility of the developed decision support system for nutrient management.

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