# Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities

arXiv: https://arxiv.org/abs/2307.08789 · HTML: https://mlsanity.com/paper/2307.08789/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers](https://mlsanity.com/paper/2204.14217/) (2022)
- [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022)
- [Photorealistic Image Synthesis for Object Instance Detection](https://mlsanity.com/paper/1902.03334/) (2019)
- [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022)
- [State-of-the-Art in the Architecture, Methods and Applications of StyleGAN](https://mlsanity.com/paper/2202.14020/) (2022)
- [LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis](https://mlsanity.com/paper/2002.10100/) (2020)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming](https://mlsanity.com/paper/2009.05750/) (2020)
- [Active Exploration for Neural Global Illumination of Variable Scenes](https://mlsanity.com/paper/2203.08272/) (2022)
- [Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation](https://mlsanity.com/paper/2104.05647/) (2021)
- [Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks](https://mlsanity.com/paper/2105.10325/) (2021)

## Built on by (most cited first, 5 of 5)

- [Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development](https://mlsanity.com/paper/2411.11285/) (2024)
- [YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning](https://mlsanity.com/paper/2410.19846/) (2024)
- [A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models](https://mlsanity.com/paper/2509.08490/) (2025)
- [Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10](https://mlsanity.com/paper/2503.00057/) (2025)
- [Melon Fruit Detection and Quality Assessment Using Generative AI-Based Image Data Augmentation](https://mlsanity.com/paper/2407.10413/) (2024)

## Abstract (arXiv)

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.