Generative diffusion models for agricultural AI: plant image generation, indoor-to-outdoor translation, and expert preference alignment

TL;DR

This paper explores diffusion-based generative models to synthesize plant images, translate indoor images to outdoor conditions, and align outputs with expert preferences, aiming to improve agricultural AI data efficiency.

Contribution

It introduces a comprehensive generative pipeline combining plant image synthesis, indoor-to-outdoor translation, and expert preference alignment for agricultural AI applications.

Findings

Synthetic images improve phenotype classification accuracy.

Translated images enhance weed detection and classification.

Reward-guided fine tuning produces more stable, expert-aligned outputs.

Abstract

The success of agricultural artificial intelligence depends heavily on large, diverse, and high-quality plant image datasets, yet collecting such data in real field conditions is costly, labor intensive, and seasonally constrained. This paper investigates diffusion-based generative modeling to address these challenges through plant image synthesis, indoor-to-outdoor translation, and expert preference aligned fine tuning. First, a Stable Diffusion model is fine tuned on captioned indoor and outdoor plant imagery to generate realistic, text conditioned images of canola and soybean. Evaluation using Inception Score, Frechet Inception Distance, and downstream phenotype classification shows that synthetic images effectively augment training data and improve accuracy. Second, we bridge the gap between high resolution indoor datasets and limited outdoor imagery using DreamBooth-based text inversion and image guided diffusion, generating translated images that enhance weed detection and classification with YOLOv8. Finally, a preference guided fine tuning framework trains a reward model on expert scores and applies reward weighted updates to produce more stable and expert aligned outputs. Together, these components demonstrate a practical pathway toward data efficient generative pipelines for agricultural AI.