ORIGINAL ARTICLE Fuzzy decision support system for fertilizer Ather Ashraf • Muhammad Akram • Mansoor Sarwar Received: 20 March 2014 / Accepted: 20 May 2014 Ó Springer-Verlag London 2014 Abstract Fuzzy geographic information systems is a newly emerging ﬁeld of computational intelligence. It combines fuzzy logic with spatial context. Most of the natural phenomena are fuzzy in nature. They show a degree of uncertainty or vagueness in their extent and attribute, which cannot be expressed by a crisp value. Agriculture is one of the ﬁelds of the spatial domain that needs to be described in fuzzy terms. Fertilizer is a key input for the agriculture sector. In this article, the spatial surfaces of fertilizers are developed for the wheat crop using a fuzzy decision support system. The algorithm of our system takes soil nutrients and cropping time as input, applies fuzzy logic on the input values, defuzziﬁes the fuzzy output to crisp value, and generates a fertilizer surface. The resultant output surface of fertilizer describes the amount of fertil- izer needed to cultivate a speciﬁc crop in a speciﬁed area. The complexity of our algorithm is OðmnrÞ, where m is the height of the raster, n is the width of the raster, and r is the number of expert rules. Keywords Soil fertilizer  Fuzzy decision support system  Geo-spatial information system  Fuzzy spatial surface  Algorithm  Fuzzy inference engine 1 Introduction Computational intelligence (CI) is a ﬁeld of intelligent processing of information related to various branches of computer science and engineering. Fuzzy inferences are one the paradigms of CI. Contemporary technologies in the ﬁeld of control systems, like industrial controllers, are promoted by using fuzzy sets [16]. Processing capacity of computer based on user input is an important aspect of fuzzy systems considered in any design of human centric computing systems. The human centricity plays a key role in the ﬁelds of intelligent data analysis and system mod- eling [22]. Fuzzy sets, introduced by Zadeh [28], provide a mech- anism for communication between computing systems and humans [7]. A fuzzy control system is developed on the basis of fuzzy set theory and fuzzy logic [29]. Many fuzzy inference systems and defuzziﬁcation techniques have already been developed. These techniques are useful in obtaining crisp output from a fuzzy input. The crisp output values are calculated using fuzzy rules applied in an inference engine using defuzziﬁcation methods [24]. Sev- eral fuzzy controllers [11, 19] have been developed using fuzzy logic and fuzzy set theory. The ﬁeld of geographic information systems (GIS) is an emerging ﬁeld of applied computer science that can be used for capturing, storing, organizing, analyzing, dis- playing and reporting spatial information. GIS based systems are important tools for spatial planning [5]. Such a systems enable planners to create and modify a land suitability analysis that makes the best use of available data. By combining the fuzzy logic concepts with GIS [2, 6, 13], an intelligent system can be designed and built, which can help planners in decision-making in the spatial context. A. Ashraf  M. Sarwar Punjab University College of Information Technology, University of the Punjab, Old Campus, Lahore 54000, Pakistan e-mail: atherashraf@gmail.com M. Sarwar e-mail: syed.sarwar@pucit.edu.pk M. Akram (&) Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan e-mail: m.akram@pucit.edu.pk; makrammath@yahoo.com 123 Neural Comput & Applic DOI 10.1007/s00521-014-1639-4