A. Markinos

A Fuzzy Cognitive Maps (FCMs) is a modelling methodology based on exploiting knowledge and experience. It comprises the main advantages of fuzzy logic and neural networks, representing a graphical model that consists of nodes-concepts (describing elements of the system) which are connected with weighted edges (representing the cause and effect relationships among the concepts). FCMs have proved to be a promising modeling methodology with many successful applications in different areas especially for simulating system design, modeling and control. In this work, FCMs are introduced to model a decision support system for precision agriculture (PA). The FCM model developed consists of nodes which describe soil properties and cotton yield and of the weighted relationships between these nodes. The nodes of the FCM model represent the main factors influencing cotton crop production i.e. essential soil properties such as texture, pH, OM, K, and P. The proposed FCM model addresses the proble...

Three experimental fields were cultivated with cotton (Gossypium hirsutum L) in Omorfochori Larissa in 2005. The correlation between electrical conductivity, plant density, plant height and yield were determined to draw conclusions and make farm decisions for the best management of the fields. The results showed significant variability in yield and Electrical Conductivity in the three fields. Electrical conductivity did not give adequate correlation to yield but it can be used to delineate management zones for targeted soil sampling. Plant height and plant density did not affect yield due to the uniformity of the plants.

This work investigates the process of yield prediction in cotton crop production using the soft computing technique of fuzzy cognitive maps. Fuzzy cognitive map (FCM) is a fusion of fuzzy logic and cognitive map theories, and is used for modeling and representing experts' knowledge. It is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. It is a challenging approach for decision making especially in complex processing environments. The FCM approach presented here was chosen to be utilized in agriculture because of the nature of the application. The prediction of yield in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. Throughout this proposed method, FCMs designed and developed to represent experts' knowledge for cotton (Gossypium hirsutum L.) yield prediction and crop management. The developed FCM model consists of nodes linke...