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.

现代农业依靠特定场地管理实践，需要准确检测、定位和量化农作物和杂草，可以通过深度学习技术实现。本研究提出了一种新颖的集成框架，利用不同的农作物和杂草模型以及可训练的元架构来结合它们的输出，从而在未知测试数据上显著提高了甘蓝作物和科冷草杂草的性能。经过消融研究的验证，证明了我们提出的模型的有效性。此外，我们还提出了两个用于比较的新颖数据集。