AgTech, the convergence of Agriculture and Technology, leads technological advancements, offering substantial potential for leveraging Artificial Intelligence (AI) and Machine Learning (ML) to transform farming practices. Despite considerable interest, the scarcity of comprehensive data remains a significant obstacle to unlocking the full potential of AI and ML applications in this domain. Our goal is to initiate the process with a limited set of images and produce a varied dataset that encapsulates the intricacies inherent in real-world deployment scenarios. These complexities include dynamic factors such as changing weather conditions, soil variations, shadows, sunlight angles and daytime conditions. The first aspect of our exploration involves the utilization of LoRA (Low-Rank Adaptation of Large Language Models)  to fine-tune a stable diffusion model with minimal training time and resources.
Our investigation also includes utilization of DinoV2 in the context of segmentation- oriented tasks, aiming to enhance segmentation models through the integration of data generated by bespoke stable diffusion models trained with LoRA. We investigate the downstream impact on model enhancement by integrating synthetic datasets. Through evaluating machine learning models on synthesized datasets, we analyze their improved performance and generalization abilities in real-world AgTech scenarios. The results highlight the potential of synthetic datasets to address the data gap and enhance the performance of machine learning models.
In summary, this research contributes to the evolving landscape of AgTech by introducing LoRA, Stable Diffusion and DinoV2 as innovative solutions to the persistent challenges of limited datasets. The proposed methodologies not only enhance model performance and generalization but also underscore the cost- effective nature of synthetic datasets in the pursuit of advancing machine learning applications in agriculture. The implications of this study extend beyond AgTech, providing a blueprint for leveraging synthetic datasets in diverse domains to optimize machine learning model development in creating domain specific large vision models(LVMs).
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