Adaptive sensor for chemical analysis

Using our experience in signal processing and optimization of complex systems we propose a new method to adaptive sensing of chemical content of vegetations. This framework is demonstrated for different agricultural plants using the neural network algorithm for classification of spectral curves and adaptive filtration. Utilization of characteristics of leaf reflectance spectrum, which are a relative characteristic of the light reflected from canopies, makes it possible to avoid the necessity of measuring the 100% reflectance standard and to provide the high resistance of the method to distorting factors in particular to soil reflectance contribution. For utilization of the method the numerical algorithms is proposed. Various estimation problems will be considered to illustrate the computational aspects of the proposed method. The software is based on digital filter, optimization approach and neural network algorithm for classification of chemical components. Supporting software for data management, storage, signal processing will be development. A concept of an intelligent sensor is considered.