Improved Crop and Weed Detection with Diverse Data Ensemble Learning

TL;DR

This paper introduces a novel ensemble learning framework using diverse crop and weed datasets to improve the generalization and accuracy of semantic segmentation models in variable field conditions, outperforming single models.

Contribution

The study proposes a new ensemble approach with a trainable meta-architecture, specifically UNET, to enhance crop and weed detection across diverse and unseen field conditions.

Findings

Ensemble models outperform single segmentation models in accuracy.

Including diverse crop and weed data improves model generalization.

UNET meta-architecture is most effective among tested configurations.

Abstract

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.