Habitat Suitability Modeling of Dominant Weed in Rapeseed (Brassica napus) Fields Using Machine Learning Techniques

Abstract

Weed infestations have been identified as a major cause of yield reductions in rapeseed (Brassica napus L.), a vital oil crop that has gained significant prominence in Iran, especially within Fars Province. Weed management using machine learning algorithms has become a crucial approach within the framework of precision agriculture for enhancing the efficacy and efficiency of weed control strategies. The evolution of habitat suitability models for weeds represents a significant advancement in agricultural technology, offering the capability to predict weed occurrence and proliferation accurately and reliably. This study focuses on the issue of dominant weed infestation in rapeseed cultivation, particularly emphasizing the prevalence and impact of wild oat (Avena fatua L.) as the dominant weed species in rapeseed farming in 2023. We collected data on 12 environmental variables related to topography, climate, and soil properties to develop habitat suitability models. Three “machine learning techniques”, including “random forest (RF)”, “support vector machine (SVM)”, and “boosted regression tree (BRT)”, were estimated based on the “receiver operating characteristic (ROC) and area under the curve (AUC)” to model the distribution of A. fatua. Model performance was quantified using the “ROC curve and AUC” metrics to identify the best predictive algorithm. The findings indicated that “Random Forest (RF), boosted regression tree (BRT), and support vector machine (SVM)” models exhibited accuracies of 99%, 97%, and 96% for the habitat suitability of A. fatua, respectively. The Boruta feature selection method identified the slope variable as significantly influential in wild oat habitat suitability modeling, followed by plan curvature, clay, temperature, and silt. This study serves as a case study that highlights the utility of machine learning for habitat suitability predictions when information on multiple environmental variables is available. This approach supports effective weed management strategies, potentially enhancing rapeseed productivity and mitigating the ecological impacts associated with weed infestation.