Trends and Future Challenges in Image Analysis for Digital Forecasting of Jeju Citrus Production

Abstract

This review paper focuses on the digital transformation of yield prediction for the sustainable development of the Jeju citrus industry, emphasizing current applications and future challenges of image analysis technologies. Accurate yield prediction is essential for stabilizing farm income, improving distribution efficiency, balancing supply and demand, and optimizing cultivation strategies. However, traditional statistics-based approaches are limited by climate change, cultivation area fluctuations, and labor shortages. In this context, Al-driven digital technologies —especially non-invasive image analysis —have emerged as promising alternatives. The paper provides an in-depth overview of image analysis applications in two key areas: fruit detection and counting, and fruit size and growth prediction. Notably, deep learning-based object detection models (e.g., YOLO, Faster R-CNN) and 3D reconstruction technologies have improved prediction accuracy. Integrating auxiliary data, such as maturity and quality indicators, is also discussed. Despite these advancements, challenges remain for real-world implementation. These include data collection under varied environments, model robustness (especially against occlusion), and the need for real-time processing and user-friendly system design. Future research should prioritize integrating heterogeneous data — including weather and soil — long-term time-series learning, and developing cost-effective, high-efficiency solutions. These efforts are expected to enhance the accuracy and reliability of citrus yield predictions, driving the digital transformation and sustainable future of the Jeju citrus industry.

Plant phenotyping relevance

画像解析による柑橘果実の検出・計数、サイズ・成長推定を中心にレビューしており、植物形質取得手法が主要内容である。

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