Anubhav Srivastava

AgTech merges Agriculture and Technology, harnessing AI and ML to revolutionize farming. Scarce comprehensive data impedes its full potential. Our focus starts with a limited image set, crafting a diverse dataset mirroring real-world complexity: weather shifts, soil variations, and lighting dynamics. Employing LoRA fine-tunes a stable diffusion model swiftly. DinoV2 enhances segmentation models by integrating LoRA-trained data. Synthetic datasets bolster model performance, evident in AgTech scenarios. This research introduces LoRA, Stable Diffusion, and DinoV2, addressing limited data challenges. These methods elevate model performance, emphasizing the cost-effectiveness of synthetic datasets. Beyond AgTech, this blueprint for synthetic datasets optimizes ML in diverse domains, paving the way for large vision models.