Evaluating Accuracy and Efficiency of Fruit Image Generation Using Generative AI Diffusion Models for Agricultural Robotics

Abstract

The automation of fruit picking using robotic systems is rapidly advancing within precision agriculture, relying heavily on the accurate detection and segmentation of fruit objects through computer vision. This study explores the use of generative artificial intelligence (AI) to create extensive fruit image datasets, concentrating on cherries, bananas, oranges, apples, and pineapples. Five generative models—Stable Diffusion v1.4, Stable Diffusion v1.5, Stable Diffusion v2, Stable Diffusion XL Refiner 1.0, and SDXL Turbo—were employed to generate 1000 images per fruit type, each set against a white background. Thresholding techniques were used to automatically extract fruit boundaries, which were then utilized to train segmentation models. The performance of these models was evaluated based on their accuracy and processing speed. SDXL Turbo consistently delivered the highest accuracy across all fruit types, though it required more processing time per image. Stable Diffusion XL Refiner 1.0 also exhibited strong accuracy but balanced performance differently. In contrast, Stable Diffusion v2 showed significant shortcomings, particularly in producing accurate images for cherries and oranges. This comparative analysis highlights the potential of advanced generative models in enhancing synthetic dataset creation for fruit object detection and segmentation in agricultural robotics. Future research will focus on refining these models to improve accuracy, broaden their applicability to a wider variety of fruits and environments, and optimize the trade-off between image quality and generation speed to meet the practical needs of agricultural automation.