Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field

Abstract

Weed infestation and their management are a critical production challenge in agricultural fields. Palmer amaranth has created management challenges because it has multiple emergence pattern and has evolved resistant to nine unique herbicide sites of action. Effective Palmer amaranth detection and positive identification in field conditions will help to improve Palmer amaranth control. A field-based hyperspectral imaging system was developed to record Palmer amaranth in soybean fields. The data were pre-processed applying Savitzky-Golay 2nd derivative, Multiplicative Scatter Correction, and Standard Normal Variate in a forward feed manner. Recursive feature elimination, SelectFromModel, sequential forward selection, and backward elimination were used to select significant wavebands from the available 224 bands. Later, supervised machine-learning models were generated to classify soybean and Palmer amaranth using the selected wavebands. Matthew’s correlation coefficient (MCC), F1 score, precision, and recall were considered as the most significant parameters to evaluate the models’ performance. The highest result was obtained by quadratic discriminant analysis with a prediction accuracy of 93.95%, a precision of 90.30%, a recall of 90.29%, an F1 score of 0.95, and an MCC score of 0.85. The findings of this study showed that the combination of hyperspectral imaging and machine-learning is a potential technique for real-time weed detection in the open field condition.