Increasing yield estimation accuracy for individual apple trees via ensemble learning and growth stage stacking

Abstract

Accurate prediction of individual apple tree yields during the preharvest stage is essential for precision orchard management and market planning. However, systematic studies focusing on apple yield estimation are scarce. To address this gap, this study targets Fuji apples in the Aksu region of Xinjiang. Multiple images were captured via a UAV during four key growth stages: flowering, fruit formation, fruit expansion, and ripening. On the basis of the extracted vegetation indices, we first developed yield estimation models using random forest (RF), support vector regression (SVR), partial least squares regression (PLS), and ridge regression (RR) methods. We subsequently combined these four models to construct a stacking ensemble learning (SEL) model. To further increase the accuracy of apple yield estimation, we refined the growth stage stacking method and developed a new model, the growth stage stacking ensemble (GSSE). This model maximises the use of spectral information from multiple apple growth stages by employing various machine learning algorithms and integrating multistage spectral data to improve yield estimation accuracy. The results indicate that the optimal period for yield estimation occurs during the fruit expansion stage, with the support vector regression (SVR) model achieving the best performance (R² = 0.654, RMSE = 5.307 kg). Compared with individual machine learning models, the SEL approach enhances yield estimation accuracy, reaching a maximum R² of 0.686 and an RMSE of 5.058 kg. Furthermore, GSSE significantly enhanced accuracy compared with the single-growth stage estimation models and SEL, with the combination of fruit expansion and fruit ripening stages yielding the best results, with an R² of 0.759 and an RMSE of 4.431 kg, with the fruit expansion stage contributing the most. This study is the first to apply the GSSE to apple yield estimation, offering novel insights for UAV-based apple yield estimation.

Plant phenotyping relevance

UAV画像と多時期スペクトル情報から個体別リンゴ収量を推定するモデルを開発・比較しており、植物形質の取得・推定手法が研究の中心である。

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