Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities
This addresses the need for realistic agricultural image datasets to support precision agriculture solutions, though it's an incremental application of existing AI methods to a new domain.
This research investigated using DALL.E's text-to-image and image-to-image generation to create agricultural image datasets, finding that image-to-image methods produced 5.78% higher PSNR (better clarity) but 10.23% lower FSIM (less structural similarity) compared to text-to-image methods.
The field of agricultural communication is evolving rapidly with the advent of generative artificial intelligence (AI), particularly image generation technologies. As these tools begin to influence how agricultural data is visualized and disseminated, the sector's diversity spanning both technical and non-technical researchers, demands a rigorous foundational study to demystify the image generation process. This research investigated the role of artificial intelligence (AI), specifically the DALL.E model by OpenAI, in advancing data generation and visualization techniques in agriculture. DALL.E, an advanced AI image generator, works alongside ChatGPT's language processing to transform text descriptions and image clues into realistic visual representations of the content. The study used both approaches of image generation: text-to-image and image-to-image (variation). Six types of datasets depicting fruit crop environment were generated. These AI-generated images were then compared against ground truth images captured by sensors in real agricultural fields. The comparison was based on Peak Signal-to-Noise Ratio (PSNR) and Feature Similarity Index (FSIM) metrics. The image-to-image generation exhibited a 5.78% increase in average PSNR over text-to-image methods, signifying superior image clarity and quality. However, this method also resulted in a 10.23% decrease in average FSIM, indicating a diminished structural and textural similarity to the original images. Similar to these measures, human evaluation also showed that images generated using image-to-image-based method were more realistic compared to those generated with text-to-image approach. The results highlighted DALL.E's potential in generating realistic agricultural image datasets and thus accelerating the development and adoption of imaging-based precision agricultural solutions.