Improved Crop and Weed Detection with Diverse Data Ensemble Learning

Muhammad Hamza Asad, Saeed Anwar, Abdul Bais; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5336-5345

Modern agriculture heavily relies on Site-Specific Farm Management practices necessitating accurate detection localization and quantification of crops and weeds in the field which can be achieved using deep learning techniques. In this regard crop and weed-specific binary segmentation models have shown promise. However uncontrolled field conditions limit their performance from one field to the other. To improve semantic model generalization existing methods augment and synthesize agricultural data to account for uncontrolled field conditions. However given highly varied field conditions these methods have limitations. To overcome the challenges of model deterioration in such conditions we propose utilizing data specific to other crops and weeds for our specific target problem. To achieve this we propose a novel ensemble framework. Our approach involves utilizing different crop and weed models trained on diverse datasets and employing a teacher-student configuration. By using homogeneous stacking of base models and a trainable meta-architecture to combine their outputs we achieve significant improvements for Canola crops and Kochia weeds on unseen test data surpassing the performance of single semantic segmentation models. We identify the UNET meta-architecture as the most effective in this context. Finally through ablation studies we demonstrate and validate the effectiveness of our proposed model. We observe that including base models trained on other target crops and weeds can help generalize the model to capture varied field conditions. Lastly we propose two novel datasets with varied conditions for comparisons. Our code will be available at github.com.