Early Apple Yield Prediction Based on Flowering Stage Image Thinning Simulation Characteristics.

Abstract

The existing fruit tree yield prediction methods mainly rely on fruit period images or long-term meteorological and soil data, which make it difficult to meet the needs of early yield prediction. In addition, the flowering period images contain complex spatial distribution and severe overlap between flowers, which makes it challenging to directly extract stable structural indicators related to yield. Most existing research has focused on simple statistical indicators such as the number of flowers, while the spatial clustering structure of flowers and their relationship with yield have not been fully explored. Therefore, this article proposes an early apple yield prediction based on flowering stage image thinning simulation characteristics. In this study, blossom images and fruit maturity yield data from 100 apple trees were collected, with flower mask images extracted through standardized image processing. First, the traditional DBSCAN clustering algorithm was enhanced by integrating a KDTree acceleration structure and an adaptive multi-scale mechanism, forming the adaptive multi-scale clustering algorithm (AMS-DBSCAN) to achieve efficient identification of flower clusters and individual flowers. Based on this, two flower thinning simulation strategies based on density and spatial uniformity were designed to model artificial thinning rules and construct multi-dimensional, interpretable phenotypic features. Then, the original statistical features were fused with strategy-generated features and optimized using Lasso. We compared multiple models including XGBoost, BPNN, and SVR for yield prediction. The experimental results showed that XGBoost achieved good predictive performance under the hybrid feature set (R 2 = 0.856, RMSE = 3.098), which was further improved to R 2 = 0.900 after feature optimization with Lasso. The results demonstrate that the proposed method enables reliable early yield estimation, providing a new reference for precision management and early decision-making in fruit tree cultivation.

Plant phenotyping relevance

花画像から花群・個体を抽出し、間引きシミュレーション由来の解釈可能な表現型特徴を構築する画像解析手法が研究の中心であり、収量推定に技術的に応用・評価されている。

Code and data availability

The paper's apple blossom images, flower masks, and yield data are explicitly not publicly available; they can only be obtained from the corresponding author on reasonable request. No public repository, code deposit, or author-hosted URL is provided in the supplied blocks.

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