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。

农业中多样数据集集成学习改进作物与杂草检测

Improved Crop and Weed Detection with Diverse Data Ensemble Learning in  Agriculture

In recent years, wildfires have posed a significant challenge due to their
increasing frequency and severity. For this reason, accurate delineation of
burned areas is crucial for environmental monitoring and post-fire assessment.
However, traditional approaches relying on binary segmentation models often
struggle to achieve robust and accurate results, especially when trained from
scratch, due to limited resources and the inherent imbalance of this
segmentation task. We propose to address these limitations in two ways: first,
we construct an ad-hoc dataset to cope with the limited resources, combining
information from Sentinel-2 feeds with Copernicus activations and other data
sources. In this dataset, we provide annotations for multiple tasks, including
burned area delineation and land cover segmentation. Second, we propose a
multitask learning framework that incorporates land cover classification as an
auxiliary task to enhance the robustness and performance of the burned area
segmentation models. We compare the performance of different models, including
UPerNet and SegFormer, demonstrating the effectiveness of our approach in
comparison to standard binary segmentation.

在本研究中，我们提出了一种多任务学习框架，通过构建一个特定数据集和引入地表覆盖分类作为辅助任务，以提高烧毁区域分割模型的鲁棒性和性能。与标准的二进制分割方法相比，我们的方法在性能上表现出了更好的效果。