An adaptive Mealy machine model for monitoring crop status

作     者：Berk Ustundag 

作者机构：Agricultural and Environmental Informatics Application & Research Center/Faculty of Informatics and Computer Engineering Istanbul Technical University Istanbul 34469 Turkey 

出 版 物：《Journal of Integrative Agriculture》 (农业科学学报（英文版）)

年 卷 期：2017年第16卷第2期

页      面：252-265页

核心收录：

学科分类：082803[工学-农业生物环境与能源工程] 08[工学] 0828[工学-农业工程] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：funded by Turkish Ministry of Development as a part of Agricultural Monitoring and Information Systems Project (2011A020100) the relevant joint project funded by Ministry of Food,Agriculture and Livestock,Turkey 

主　　题：plant phenology monitoring yield prediction finite automata Mealy machine remote sensing 

摘      要：Variation in phenological stage is the major nonlinearity in monitoring, modeling and various estimations of agricultural systems. Indices are used as a common means of evaluating agricultural monitoring data from remote sensing and terrestrial observation systems, and many of these indices have linear characteristics. The analysis of and relationships between indices are dependent on the type of plant, but they are also highly variable with respect to its phenologicat stage. For this reason, variations in the phenologica! stage affect the performance of spatiotemporal crop status monitoring. We hereby propose an adaptive event-triggered model for monitoring crop status based on remote sensing data and terrestrial observations. In the proposed model, the estimation of phenological stage is a part of predicting crop status, and spatially distributed remote sensing parameters and temporal terrestrial monitoring data are used together as inputs in a state space system model. The temporal data are segmented with respect to the phenological stage-oriented timing of the spatial data, so instead of a generalized discrete state space model, we used logical states combined with analog inputs and adaptive trigger functions, as in the case of a Mealy machine model. This provides the necessary nonlinearity for the state transi- tions. The results showed that observation parameters have considerably greater significance in crop status monitoring with respect to conventional agricultural data fusion techniques.