A robust knowledge-based plant searching strategy

This paper presents a knowledge-based plant information retrieval system that is robust to inaccurate and erroneous user queries. First, a knowledge-based genetic algorithm (GA) corrects the erroneous input vectors before these are fed into a back-propagation neural network (BPNN) that performs the actual query. Experimental results show that the strategy achieves a 75% recall rate and 25% precision rate with a cutoff level of 10 under the misjudgment of shapes. Moreover, a fully trained BPNN dynamically adapts to changes in the environment. Due to its robust and simple user interface and portability, the strategy is particularly applicable to educational settings such as outdoor fieldwork in courses on ecology.