Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model

• URL: http://arxiv.org/abs/2307.08789v4
• Date: Tue, 27 Aug 2024 16:43:17 GMT
• Title: Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model
• Authors: Ranjan Sapkota, Manoj Karkee,
• Abstract summary: The study used both approaches of image generation: text-to-image and image-to image (variation) Images generated using image-to-image-based method were more realistic compared to those generated with text-to-image approach.
• Score: 0.4143603294943439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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