A review of machine learning techniques for identifying weeds in corn

Abstract

Weeds pose a major challenge in achieving high yield production in corn. The use of herbicides although effective can be expensive and their excessive use poses ecological concerns and herbicide resistance. Precise identification of weeds using Machine Learning (ML) models significantly reduces the use of herbicides. In this study, we provide a brief overview of the important ML methods used for identifying weeds in corn i.e., classification and object detection. The various metrics that are used for the evaluation of the performance of ML methods are also discussed. In the end, we identify some important research gaps which warrant future investigation. Most ML methods for the identification of weeds use digital images as input data, however, in some cases, hyperspectral data were used. Most of the current studies employ support vector machines and neural networks for the identification of weeds. Classification accuracy and F1 score are the two most frequently used accuracy metrics to evaluate the performance of ML models used. Future research on the identification of weeds may focus on improving the data volume using data augmentation, transfer learning to benefit from existing models, and interpretability of neural networks to avoid overfitting and make models more transparent.