Ensemble learning-based approach for automatic classification of termite mushrooms

Termite mushrooms are edible fungi that provide significant economic, nutritional, and medicinal value. However, identifying these mushroom species based on morphology and traditional knowledge is ineffective due to their short development time and seasonal nature. This study proposes a novel method for classifying termite mushroom species. The method utilizes Gradient Boosting machine learning techniques and sequence encoding on the Internal Transcribed Spacer (ITS) gene dataset to construct a machine learning model for identifying termite mushroom species. The model is trained using ITS sequences obtained from the National Center for Biotechnology Information (NCBI) and the Barcode of Life Data Systems (BOLD). Ensemble learning techniques are applied to classify termite mushroom species. The proposed model achieves good results on the test dataset, with an accuracy of 0.91 and an average AUCROC value of 0.99. To validate the model, eight ITS sequences collected from termite mushroom samples in An Linh commune, Phu Giao district, Binh Duong province, Vietnam were used as the test data. The results show consistent species identification with predictions from the NCBI BLAST software. The results of species identification were consistent with the NCBI BLAST prediction software. This machine-learning model shows promise as an automatic solution for classifying termite mushroom species. It can help researchers better understand the local growth of these termite mushrooms and develop conservation plans for this rare and valuable plant resource.