  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.
