LAUSR

Johnsongrass (Sorghum halepense) is one of the weeds that evolves resistance to glyphosate [N-(phosphonomethyl)-glycine], the most widely used herbicide, and the weed may cause agronomic troublesome in the southern United States. This paper reports a study on developing a hyperspectral plant sensing approach to explore the spectral features of glyphosate-resistant (GR) and glyphosate-sensitive (GS) plants to evaluate this approach using machine learning algorithms to differentiate between GR and GS plants. In average GR plants have higher spectral reflectance compared with GS plants. The sensitive spectral bands were optimally selected using the successive projections algorithm respectively wrapped with the machine learning algorithms of k-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM) with Fisher linear discriminant analysis (FLDA) to classify between GS and GS plants. At 3 WAT (weeks after transplanting) KNN and SVM could not acceptably classify the GR and GS plants but they improved significantly with the stages to have their overall accuracies reached 73% and 77% respectively at 5 WAT. RF and FLDA had a better ability to classify the plants at 3 WAT but RF was low in accuracy at 2 WAT while FLDA dropped the accuracy to 50% at 4 WAT from 57% at 3 WAT and raised to 73% at 5 WAT. Previous studies were conducted developing the hyperspectral imaging approach to differentiate GR Palmer amaranth from GS Palmer amaranth and GR Italian ryegrass from GS Italian ryegrass with classification accuracies of 90% and 80%, respectively. This study demonstrated that the hyperspectral plant sensing approach could be developed to differentiate GR johnsongrass from glyphosate-sensitive GS ones with the highest classification accuracy of 77%. The comparison with our previous studies indicated that the similar hyperspectral approach could be used and transferred from classification across different GR and GS weed biotypes, such as Palmer amaranth, Italian ryegrass and johnsongrass. So, it is highly possible for classification of more other GR and GS weed biotypes as well. On the basis of classic pattern recognition approaches the process of plant classification can be enhanced by modeling using machine learning algorithms.