## 4 Discussion  
Precision agriculture aims to integrate advanced technologies to address various challenges in the agricultural sector, enhancing productivity, efficiency, and profitability while minimizing waste and environmental impacts. Deep learning approaches play a crucial role in enabling automated decision-making capabilities. By automating the processing of visual data from agricultural fields, these decision-making processes can be significantly improved and scaled up. However, the development of DL-based techniques encounters challenges stemming from the dynamic nature of agricultural fields, characterized by diverse growth stages, inconsistent weather conditions, and variable lighting. As such, models developed based on one snapshot of these fields are often not generalizable across different growth stages or  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Domain** & **Model** & **Dice Score** & **Domain** & **Model** & **Dice Score** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **  
environmental conditions. Furthermore, developing large-scale annotated datasets across various growth stages and environmental conditions is time-consuming and expensive. These challenges hinder the development of generalizable DL-based solutions for various agricultural tasks. Consequently, developing methodologies that allow DL-based solutions to generalize across different conditions (domains) could facilitate the widespread adoption of these technologies in the agricultural sector. In response to these challenges, we developed a semi-self-supervised domain adaptation technique that employs deep convolutional neural networks and a probabilistic diffusion process. This approach was developed with minimal data annotation, requiring only three manually annotated images. We classify this method as semi-supervised because it leverages a small amount of annotated data (three images) alongside a significant volume of unannotated data. Additionally, this method can be considered as self-supervised as it utilizes computationally annotated data for model development. The proposed model demonstrated substantial improvements in performance compared to the baseline model presented in recent work by [PERSON] et al. [7], while also showing a lower variance in model performance across 18 different domains from the GWHD dataset. These 18 domains represent various growth stages of wheat fields and different environmental conditions. In this study, we devised a two-phase model training process. Initially, model \(\mathcal{F}_{\eta+\rho_{1}}\) was developed by synthesizing a large number of computationally annotated samples resulting from a single sample for image synthesis and validation, showcasing a nearly 20% improvement over a recent work by [PERSON] et al. [7] on the target domain. Subsequently, in the second phase, model \(\mathcal{F}_{\eta+\zeta+\rho}\) leveraged additional computationally generated data, further enhancing the performance difference to 28%. As presented in Table 1, our final model, \(\mathcal{F}_{\eta+\zeta+\rho}\), shows a 28.1% improvement in the Dice score and a 25.2% improvement in the IoU when compared to model \(\mathcal{S}\) from [PERSON] et al. The increased performance showcases the utility of the proposed semi-self-supervised domain adaptation technique. Label noise poses a significant challenge in semi-supervised domain adaptation, whether it stems from manual annotation errors or errors introduced by the use of non-curated pseudo-labeled data for model fine-tuning. The presence of label noise in manually annotated data can severely degrade the performance of deep learning models by introducing incorrect information during the training phase, leading to suboptimal decision boundaries and poor generalization to the target domain. Utilizing pseudo-labels without any form of curation may also further exacerbate this issue, as these uncurated labels can propagate errors and amplify the negative impact of label noise.