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Pesticide usage is inevitable in agriculture when there is a crop loss. The main objectives of applying pesticides in agricultural fields are (Damalas & Eleftherohorinos, 2011) to control weeds, prevent diseases, control pests, control other plant pathogens, and to increase the agricultural yield. Pesticides are targeted to kill pests in the field but non targeted species (Aktar, Sengupta, & Chowdhury, 2009) such as earthworms (considered to be the friend of farmers), humans, birds, animals and environment are also getting affected by pesticides. Pesticides enter into human body through inhalation and ingesting fruits, vegetables, grains and so on. When exposing to pesticides, the initial symptoms are eye irritation, skin allergy, difficulty in breathing, loss of consciousness and seizures. The recent studies have revealed that over exposure to pesticides may lead to chronic non epidemic diseases such as cancer (Sabarwal, Kumar, & Singh, 2018), Parkinson’s disease, Alzheimer’s disease, Reproductive disorders, Respiratory disorders, genetic damages, etc., it is subtle to directly associate the exposure to pesticides and their hazardous effects. Hence, fuzzy set theory is adapted to deal the uncertainty which is raised in the present study.
Fuzzy logic was first proposed by Zadeh in 1965 (Zadeh, 1965) order to tackle vagueness in the real-life situations. The theory of fuzzy logic can be used to determine the hazards of human health by applying chemical pesticides to the agricultural lands using fuzzy decision-making system. Environmental impact of using pesticides in the agricultural field crop was determined by an indicator “Ipest” by van der Werf and Zimmer in 1998 using fuzzy expert system (Van Der Werf & Zimmer, 1998). Fuzzy multi criterion decision making system was adapted for water management for agricultural lands by Gupta et.al., in 2000 (Gupta, Harboe, & Tabucanon, 2000). Fuzzy decision-making system was used to assess the environmental risk by the application of pesticides in the field by Finizio and Villa in 2002 (Finizio & Villa, 2002). In 2003, Ferraro et.al., have developed the set of indicators of the impact of pesticide uses on the environment and assessed the indicators with the aid of fuzzy logic (Ferraro, Ghersa, & Sznaider, 2003). In 2004, Biswas and Pal solved agriculture land use planning for optimal production using fuzzy goal programming model (Biswas & Pal, 2005).
Ground water pollution risk assessment was evaluated using fuzzy decision support system by Uricchio et.al., in 2004 (Uricchio, Giordano, & Lopez, 2004). In 2005, Prato proposed an alternative method for determining the good sustainability of an ecological system using fuzzy logic (Prato, 2005). In 2008, Kouloumpis et al., (Kouloumpis, Kouikoglou, & Phillis, 2008) used sustainability assessment by fuzzy evaluation (SAFE) for evaluating overall sustainability by evaluating two primary components ecological sustainability and human sustainability, also put the ranking to the countries according to their overall sustainability. Liu et.al., (Liu, Liang, Yeh, & Chen, 2009) utilized fuzzy logic to assess environmental impact with decision support method and “if then” rules were created to evaluate the importance of environmental impacts in 2009. Fuzzy expert system was used to determine the environmental risk of pesticide uses on different kinds of crops using IPEST index by Arregui et al., in 2010 (Arregui, Sánchez, Althaus, Scotta, & Bertolaccini, 2010). Papageorgiou et al. (Papageorgiou, Markinos, & Gemtos, 2011) investigated the method for high yield cotton crop production with the aid of fuzzy cognitive maps and predicted that this method was convenient for high yield cotton production in 2011. Due to contamination of uncontrolled landfills, there were vagueness in determining most vulnerable area, therefore fuzzy analysis was applied to compute the most vulnerable area for reclamation action (Caniani, Lioi, Mancini, & Masi, 2011).