Frontiers and advances of deep learning-based fruit and vegetable image analysis

Abstract

Deep learning has achieved promising performance for fruit and vegetable image analysis, by possessing strong representation power, and providing resilient generalization and broad transferability on large-scale data for classification, detection, and segmentation tasks, which is indispensable role in optimizing agricultural practices. This comprehensive survey reviews over 270 recent studies, offering a deep exploration of the key techniques and strategies, fundamental properties, and advancements and future directions according to different categories of deep learning methods for fruit and vegetable image analysis. Furthermore, this paper outlines the novelty and concept of fruit and vegetable image analysis, summarizes publicly available datasets, evaluation metrics, and discusses successful applications in disease detection, quality grading, yield estimation, localization, and multiple application integration. The survey emphasizes the need for processing large-scale datasets and exploring the potential of efficient deep learning for enhancing real-time applications and specific tasks. By comprehensively comparing and analyzing the fundamental attributes of the fruit and vegetable image analysis methods from a fresh perspective, this survey reveals the commonalities and disparities of divert techniques and guides researchers and practitioners toward developing more efficient and accurate solutions.

Plant phenotyping relevance

果実・野菜画像解析の深層学習手法を包括的にレビューし、疾患検出や収量推定など植物の状態・形質推定、データセット、評価指標を扱うため、フェノタイピング手法レビューとして中心的です。

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