Machine learning techniques in plant biology

Abstract

There is an increasing amount of various genome-sequencing projects and advancement in generation of plant ESTs has resulted in generation of large quantities of data from different fields of plant biology in the public domain. Therefore, a need arises in the analysis of the available data and integrating them with several information of plant biology like crop improvement, nutrigenomics, biochemical engineering, etc. The biological data are mostly complex and vague, analysis of these data is difficult, and interpretation of interaction in different elements cannot be done by simple mathematical functions. Complex computing approaches like artificial intelligence are being applied to understand and interpret these data. The definition of intelligence is debatable for a long period of time; however, intelligence can be vaguely defined as the ability to learn from previous experiences and to adapt accordingly in relatively new situations. Artificial intelligence uses machine learning algorithm in which the system generates some adaptive learning approaches in order to achieve some goal of environment. Several machine learning approaches have been applied in plant biology till date. In this chapter we will discuss few machine learning approaches and their applications in plant biology.