Behavior patterns related to the agricultural practices in the production of Persian lime (Citrus latifolia tanaka) in the seasonal orchard

Display Omitted The behavior patterns in the Persian lime production process are studied.An expert system (ES) based on fuzzy logic is developed.The interrelationship of the agricultural practices is highlighted.The productivity of the orchard increases when the "pruning" of the tree is performed prior to fertilization.The producer decisions in the production of Persian lime can be improved. Production of the Persian lime (Citrus latifolia tanaka) has been the main objective of several studies related to the problem of low performance of yield and fruit quality in the orchard, attributed among different technological factors to the minimal application of Good Agricultural Practices (GAP) and to the cultural aspects of the producer. This paper contributes to the recognition of the behavior patterns of GAP for seasonal orchard (SO), to allow the Persian lime producers to make the right decisions assessing and improving the management of their orchards. To identify the behavior patterns in the Persian lime production process an expert system (ES) based on fuzzy logic proposed by Fernandez et al. (2014) has been used, in which a set of inference rules based on the knowledge of experts in this field is encoded to explain the interrelationship of the agricultural practices and uncertainties in the production of Persian lime: Pruning, Soil nutrition, Pests Control, Planting density, Tree production, Wind, Rainfall. The ES simulates from agricultural practices and uncertainties, the Persian lime production system in three stages of fruit growth, which represent the fuzzy models of the ES: flowering, bud, and fruit. The manipulation of the agricultural practices in the ES allowed to model production scenarios for SO of Persian lime, and helped to identify behavior patterns in these practices with production yield and fruit quality. The results demonstrate that if prior to fertilization, the practice of "pruning" the tree is performed, orchard productivity increases. However, when the "pruning" (aesthetics or stressful) is performed less than 50mmmonth-1 of rain, even in optimal conditions of application of nutrients and pest control, the production yield is similar. The modeling scenarios of the ES provide information regarding behavior patterns to the producer, and the interrelation of agricultural practices in uncertain environments of rain and wind in order to improve the decision-making process in Persian lime production.

[1]  J. Syvertsen,et al.  Multiple abiotic stresses occurring with salinity stress in citrus , 2014 .

[2]  Catherine Azzaro-Pantel,et al.  A Multi-Objective Modelling and Optimization Framework for Operations Management of a Fresh Fruit Supply Chain: A Case Study on a Mexican Lime Company , 2014 .

[3]  S. Consoli,et al.  Sustainable management of limited water resources in a young orange orchard , 2014 .

[4]  G. Almaguer-Vargas,et al.  DESFASAMIENTO DE COSECHA DE LIMÓN PERSA , 2011 .

[5]  M. Carr THE WATER RELATIONS AND IRRIGATION REQUIREMENTS OF CITRUS (CITRUS SPP.): A REVIEW , 2012, Experimental Agriculture.

[6]  Shalabh Gupta,et al.  Behavioral pattern identification for structural health monitoring in complex systems , 2006 .

[7]  Gerrit Hoogenboom,et al.  A web-based fuzzy expert system for frost warnings in horticultural crops , 2012, Environ. Model. Softw..

[8]  Catherine Azzaro-Pantel,et al.  An expert system for predicting orchard yield and fruit quality and its impact on the Persian lime supply chain , 2014, Eng. Appl. Artif. Intell..

[9]  A. Srivastava Integrated Nutrient Management in Citrus , 2012 .

[10]  O. Bruno,et al.  Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants , 2014 .

[11]  Dilip Kumar Chakrabarti,et al.  A brief survey of computerized expert systems for crop protection being used in India , 2008 .

[12]  M. Balakrishnan,et al.  Development of an Expert System for Agricultural Commodities , 2013 .

[13]  E. S. Stuchi,et al.  The horticultural performance of five ‘Tahiti’ lime selections grafted onto ‘Swingle’ citrumelo under irrigated and non-irrigated conditions , 2013 .

[14]  Riza Sulaiman,et al.  A framework of an expert system for crop pest and disease management , 2013 .

[15]  Jan G. Bazan Behavioral Pattern Identification Through Rough Set Modeling , 2005, Fundam. Informaticae.

[16]  Elpiniki I. Papageorgiou,et al.  Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application , 2011, Appl. Soft Comput..