Application of computational intelligence in plant growth modelling

Nowadays, systems are being developed intelligently through the use of computational intelligence (CI). The central scientific goal of CI is to understand the principles that make intelligent behavior possible in natural or artificial systems. The growth environment of plants affects their survival, development, productivity and quality. Therefore, an understanding of the different balances of these different types of factors is necessary to allow a precise analysis of the plant condition in different growth environments. Crop growth is under a complex system that many variables are contributing to it. Variables in this system are strongly interdependent and this makes it difficult to know exactly which inputs contribute to an observed output and its contribution extent. With this, there is a need to develop an intelligent model that is capable of filtering noise and capable to come up with solution even though there is a limitation on its parameters. This study is focused on modelling the related factors for the crop growth of lettuce using various computational intelligence such as artificial neural network, genetic algorithm and adaptive neuro-fuzzy inference system. The pre-harvest factors such as temperature, light intensity and carbon dioxide are considered as input in modelling the crop growth. Also, a vision system is used to obtain the image of the lettuce for the quality assessment and crop stage determination. The canopy measurement which is related to the crop stage and yield is done on this study