Early Detection of Herbicide Resistance Evolution in Rigid Ryegrass (Lolium rigidum) Using Sensor-Based Smart Farming for Sustainable Weed Management

Lolium rigidum is among the most prevalent and noxious weeds in cereal and perennial cropping systems worldwide and has developed resistance to several herbicide modes of action. This study employed a sensor-based smart farming method for the early screening of herbicide resistance across three L. rigidum accessions in Greece, followed by dose–response experiments with clodinafop-propargyl, glyphosate, and mesosulfuron-methyl + iodosulfuron-methyl. In the preliminary screening, herbicides were applied at their highest recommended rates, whereas the dose–response experiments included five application rates (0, 1/4X, X, 2X, and 4X). The EM2 accession exhibited confirmed resistance to mesosulfuron-methyl + iodosulfuron-methyl, with a resistance index of 5.31 and a five-fold increase in the herbicide rate required compared to the susceptible EM1 accession. For clodinafop-propargyl, the GR50 value of the resistant EM3 accession (147.97 g a.i. ha−1) was approximately 2.5-fold higher than that of the susceptible EM2 accession (60.28 g a.i. ha−1). Glyphosate application provided only partial biomass reduction in resistant accessions, indicating reduced susceptibility. In parallel, TaqMan assays were developed and validated to detect target-site mutations linked to resistance against EPSPS-, ACCase-, and ALS-inhibiting herbicides, supporting the molecular interpretation of the observed resistance patterns. Overall, the results demonstrate that sensor-based smart farming approaches can provide a rapid and reliable tool for the early screening of herbicide resistance, enabling more informed crop protection strategies and supporting sustainable weed management. Further research across diverse soil types and climatic conditions is warranted to validate and extend the applicability of these approaches.