Open Access

Artificial Intelligence in Sustainable Fruit Growing: Innovations, Applications, and Future Prospects

1 Transilvania University of Brasov, Faculty of Silviculture and Forest Engineering, Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Romania

Abstract

The global demand for nutritious food, coupled with environmental and economic constraints, has driven the need for sustainable agricultural practices, particularly in fruit growing. Artificial intelligence (AI) has emerged as a transformative technology to enhance the sustainability and efficiency of fruit production. This review explores the current landscape of AI applications in sustainable fruit growing, focusing on innovations, practical applications, and future prospects. Key AI technologies, including machine learning, computer vision, robotics, and data analytics, are analyzed for their roles in precision agriculture, pest and disease management, yield prediction, and automated orchard management. Notable advancements include AI models achieving over 98% accuracy in detecting pomegranate fruit diseases and robotics reducing labor costs by up to 95%. These applications contribute to environmental sustainability by minimizing resource waste and chemical use, while also improving economic viability and social well-being. However, challenges such as high costs, data requirements, and technical expertise gaps hinder widespread adoption. Future directions involve developing robust, interpretable AI models, integrating with emerging technologies like IoT and blockchain, and addressing climate change and evolving agricultural challenges. This review underscores AI’s potential to revolutionize sustainable fruit growing, ensuring resilient and environmentally friendly fruit production to meet global food demands.

Keywords

How to Cite

KESKES , M. I. (2025). Artificial Intelligence in Sustainable Fruit Growing: Innovations, Applications, and Future Prospects. MAS Journal of Applied Sciences, 10(2), 259–272. https://doi.org/10.5281/zenodo.15667640

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