Research on section-based canopy leaf area online calculation model for the whole growth period of fruit trees

Abstract

Fruit tree canopy leaf area is an important metric for calculating airflow and pesticide dose for accurate variable-rate applications (VRAs) in orchard air-assisted spraying. Existing canopy leaf area calculation models have been established based on the data of a single growth period and the whole canopy leaf area, and it is difficult to meet the precise VRA needs of orchard spraying during whole growth period of fruit trees. In this study, a feature information detection system for fruit tree canopies was designed based on light detection and ranging (LiDAR). Canopy leaf area and LiDAR point cloud detection tests were carried out on peach trees during their whole growth period. The changes of area of individual leaves, leaf number, LiDAR point clouds, and section-based canopy leaf areas and volumes at different growth stages were obtained. Based on least squares regression (LSR) Gaussian fitting and backpropagation (BP) neural network methods, the online calculation models of section-based canopy leaf area was established, and a model modification method was proposed. The R² values of the LSR and BP models increased from 0.865 and 0.863 to 0.906 and 0.898, respectively, and the root-mean-square error (RMSE) decreased from 5110.65 cm² and 5208.74 cm² to 4325.37 cm² and 4600.74 cm², respectively. The accuracy of the model constructed by LSR Gaussian fitting was relatively high, and it was easier to deploy in VRA programs. Compared with those of the existing calculation models, the calculation accuracy and generality of the model constructed in this paper are improved, thus providing model support for the research and development of airflow and pesticide dose on-demand control systems for orchard precision variable-rate spraying.

Plant phenotyping relevance

LiDARを用いた果樹キャノピーの葉面積検出システムと、成長期間全体で葉面積をオンライン推定するモデルを開発・検証しており、植物形質の取得・抽出手法が研究の中心である。

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