Herbicide-resistant weeds are fast becoming a substantial global problem, causing significant crop losses and food insecurity. Late detection of resistant weeds leads to increasing economic losses. Traditionally, genetic sequencing and herbicide dose-response studies are used to detect herbicide-resistant weeds, but these are expensive and slow processes. To address this problem, an artificial intelligence (AI)-based herbicide-resistant weed identifier program (HRIP) was developed to quickly and accurately distinguish common chickweed plants that are resistant to acetolactate synthase (ALS) inhibitor herbicides and those that are susceptible to ALS inhibitors. A regular camera was converted to capture light wavelengths from 300 to 1,100 nm. Full-spectrum images from a 2-yr experiment were used to develop a hyperparameter-tuned convolutional neural network model using a “train from scratch” approach. This novel approach exploits the subtle differences in the spectral signature of ALS inhibitor-resistant and ALS inhibitor-susceptible common chickweed plants as they react differently to the ALS-inhibiting herbicide treatments. The HRIP was able to identify ALS-inhibitor–resistant common chickweed as early as 72 h after treatment at an accuracy of 88%. It has broad applicability due to its ability to distinguish common chickweed plants that are resistant to ALS-inhibitor herbicides from those that are susceptible to becoming resistant to them regardless of the type of ALS herbicide or dose used. Using tools such as the HRIP will allow farmers to make timely interventions to prevent the herbicide-escape plants from completing their life cycle and adding to the weed seedbank.

Nomenclature: common chickweed, Stellaria media (L.) Vill.