Automatic detection of weeds: synergy between EfficientNet and transfer learning to enhance the prediction accuracy

Abstract The application of digital technologies to facilitate farming activities has been on the rise in recent years. Among different tasks, the classification of weeds is a prerequisite for smart farming, and various techniques have been proposed to automatically detect weeds from images. However, many studies deal with weed images collected in the laboratory settings, and this might not be applicable to real-world scenarios. In this sense, there is still the need for robust classification systems that can be deployed in the field. In this work, we propose a practical solution to recognition of weeds exploiting two versions of EfficientNet as the recommendation engine. More importantly, to make the learning more effective, we also utilize different transfer learning strategies. The final aim is to build an expert system capable of accurately detecting weeds from lively captured images. We evaluate the approach's performance using DeepWeeds, a real-world dataset with 17,509 images. The experimental results show that the application of EfficientNet and transfer learning on the considered dataset substantially improves the overall prediction accuracy in various settings. Through the evaluation, we also demonstrate that the conceived tool outperforms various state-of-the-art baselines. We expect that the proposed framework can be installed in robots to work on rice fields in Vietnam, allowing farmers to find and eliminate weeds in an automatic manner.