Fuzzy decision support system for improving the crop productivity and efficient use of fertilizers

Abstract This work investigates the process of reducing the fertilizer consumption and improving the crop productivity using the fuzzy logic systems. The system comprises two parts; land report based expert knowledge to stimulate the yield potential through appropriate organic lacking minerals in soil. The system structure consists of 8 parallel systems. The integrated knowledge and formation of fuzzy rules were based on multiple domain cores professionals – water, soil and agronomy with expert farmer interviews. This research work is to improve the productivity with minimum consumption of fertilizer. The study has been carried out to access the fertilizer consumption in both the ACZ(Agro Climatic Zone) with an exhaustive daily filed measurements and lab analysis for a duration of three years to determine exact fertilizer need for every individual lands. The above data was analysed in MATLAB to establish feasibility rules for decision support systems for the crops to get the targeted output.

[1]  J. Ardö,et al.  Crop Yield Gaps in Cameroon , 2014, AMBIO.

[2]  P. Beckett,et al.  Combining soil map and soil analysis for improved yield prediction , 1988 .

[3]  T. Iizumi,et al.  How do weather and climate influence cropping area and intensity , 2015 .

[4]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[5]  O. Salazar,et al.  Evaluation of soil fertility and fertilisation practices for irrigated maize (Zea mays L.) under Mediterranean conditions in Central Chile , 2015 .

[6]  R. Lal,et al.  Effects of soil erosion on crop productivity , 1987 .

[7]  S. Savcı.  Investigation of Effect of Chemical Fertilizers on Environment , 2012 .

[8]  R. Rivett,et al.  A Fuzzy Logic Fog Forecasting Model for Perth Airport , 2012, Pure and Applied Geophysics.

[9]  Jianbo Shen,et al.  Closing yield gaps in China by empowering smallholder farmers , 2016, Nature.

[10]  E. Simonne,et al.  Localized Application of Fertilizers in Vegetable Crop Production , 2017 .

[11]  M. Picanço,et al.  Key factors affecting watermelon yield loss in different growing seasons , 2017 .

[12]  Dionissios Kalivas,et al.  Decision support system for nitrogen fertilization using fuzzy theory , 2011 .

[13]  Eyal Ben-Dor,et al.  Application in Analysis of Soils , 2015 .

[14]  P. Patnaik.  Handbook of Environmental Analysis: Chemical Pollutants in Air, Water, Soil, and Solid Wastes , 1997 .

[15]  S. Sivakami,et al.  Evaluating the effectiveness of expert system for performing agricultural extension services in India , 2009, Expert Syst. Appl..

[16]  V. D. Fageria.  NUTRIENT INTERACTIONS IN CROP PLANTS , 2001 .

[17]  Xin-ping Chen,et al.  Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. , 2012, Journal of experimental botany.

[18]  R. Ankaiah,et al.  A framework of information technology-based agriculture information dissemination system to improve crop productivity , 2005 .

[19]  A. Wallace.  Soil acidification from use of too much fertilizer , 1994 .

[20]  Hiroaki Ishii,et al.  A Crop Planning Problem with Fuzzy Random Profit Coefficients , 2005, Fuzzy Optim. Decis. Mak..

[21]  F. Eulenstein,et al.  Nutrient Balances in Agriculture: A Basis for the Efficiency Survey of Agricultural Groundwater Conservation Measures , 2014 .

[22]  R. Cohen,et al.  Effect of soil temperature on disease development in melon plants infected by Monosporascus cannonballus , 2002 .

[23]  A. Azadeh,et al.  An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption , 2013 .

[24]  Elpiniki I. Papageorgiou,et al.  Hybrid learning of fuzzy cognitive maps for sugarcane yield classification , 2016, Comput. Electron. Agric..

[25]  P. Dey,et al.  Effect of soil-test-based long-term fertilization on soil health and performance of rice crop in Vertisols of central India , 2016 .

[26]  D. Cleveland,et al.  Farmer Selection and Conservation of Crop Varieties , 2004 .

[27]  S. Wani,et al.  Enhancing Resource Use Efficiency Through Soil Management for Improving Livelihoods , 2017 .

[28]  Muhammad Akram,et al.  Fuzzy decision support system for fertilizer , 2014, Neural Computing and Applications.

[29]  Ruth Delzeit,et al.  Addressing future trade-offs between biodiversity and cropland expansion to improve food security , 2016, Regional Environmental Change.

[30]  Muhammad Akram,et al.  Fuzzy Climate Decision Support Systems for Tomatoes in High Tunnels , 2017, Int. J. Fuzzy Syst..

[31]  H. Maduakor.  Efficient fertilizer use for increased crop production: the humid Nigeria experience. , 1991 .

[32]  Ali T. Ayoub,et al.  Fertilizers and the environment , 1999, Nutrient Cycling in Agroecosystems.