Rapid Detection of Acetolactate Synthetase Inhibitor Resistant Weeds Utilizing Novel Full-Spectrum Imaging and a Hyperparameter-Tuned Convolutional Neural Network (CNN)

Abstract

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 acetolactate synthetase inhibitor (ALS)-resistant from -susceptible common chickweed plants. A regular camera was converted to capture light wavelengths from 300 to 1,100 nm. Full spectrum images from a two-year experiment were used to develop a hyperparameter-tuned convolutional neural network (CNN) model utilizing a “train from scratch” approach. This novel approach exploits the subtle differences in the spectral signature of ALS-resistant and -susceptible common chickweed plants as they react differently to the ALS herbicide treatments. The HRIP was able to identify ALS-resistant common chickweed as early as 72 hours after treatment at an accuracy of 88%. It has broad applicability due to its ability to distinguish ALS-resistant from -susceptible common chickweed plants regardless of the type of ALS herbicide or dose used. Utilizing 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.