SIMCE: An expert system for seedling weed identification in cereals

Abstract

Identification of weed seedlings is a difficult task. An expert system to help farmers and extension workers to identify weed species in cereals has been developed. The expert system uses a hierarchical classification and a mix of the text description, photographs and artistic pictures. The system is supported by a data base containing information about 41 weed species and 128 colour images. The expert system was evaluated following the conventional expert system evaluation methodologies. Results of the validation indicated that non-expert users were able to make identification using the expert system. A total of 149 identifications were performed and 63% were identified correctly. The erroneous identifications tended to cluster around monocot species; especially Avena sterilis, Lolium rigidum, Phalaris ssp. and Bromus sterilis were misidentified. Results of the validation process and the writing suggestions provided by the participants were used to implement improvements in the system. The program can be used as an identification tool for farmers and technicians and for educational purposes.