【摘要】：為給果園精細(xì)管理中果樹(shù)修枝整形、果實(shí)品質(zhì)評(píng)價(jià)以及果實(shí)產(chǎn)量估算等提供科學(xué)的理論依據(jù)和技術(shù)指導(dǎo),以果園自然開(kāi)心形蘋(píng)果樹(shù)為研究對(duì)象,基于果樹(shù)三維點(diǎn)云結(jié)構(gòu),進(jìn)行果樹(shù)冠層空間光照分布建模研究。用三維點(diǎn)云重構(gòu)技術(shù)和點(diǎn)云分割技術(shù)獲取果樹(shù)不同高度的點(diǎn)云分層,分別使用像素占比和Graham掃描算法計(jì)算各高度點(diǎn)云分層垂直投影的有效投影面積和占地面積及有效葉面積指數(shù)。以果樹(shù)冠層不同高度層的有效葉面積指數(shù)為自變量,對(duì)不同高度層平均相對(duì)光照強(qiáng)度進(jìn)行線性回歸,獲得果樹(shù)冠層光照分布模型,并對(duì)模型進(jìn)行驗(yàn)證。結(jié)果表明:所建果樹(shù)冠層光照分布模型的校正決定系數(shù)R2c為0.924,校正均方根誤差RMSEC為0.05,驗(yàn)證決定系數(shù)R2v為0.955,驗(yàn)證均方根誤差RMSEP為0.04,相對(duì)分析誤差RPD為4.91。該模型具有較高的預(yù)測(cè)精度和較強(qiáng)的預(yù)測(cè)能力。
[Abstract]:In order to provide scientific theoretical basis and technical guidance for fruit tree pruning and shaping, fruit quality evaluation and fruit yield estimation in the fine management of orchard, the natural happy apple tree in orchard was taken as the research object, based on the three-dimension point cloud structure of fruit tree. The spatial light distribution modeling of fruit tree canopy was carried out. Three dimensional point cloud reconstruction technique and point cloud segmentation technique were used to obtain point cloud stratification of different height of fruit trees. The effective projection area, the occupied area and the effective leaf area index of each height point cloud stratified vertical projection are calculated by using pixel duty ratio and Graham scanning algorithm respectively. Taking the effective leaf area index of different height layers of fruit tree as independent variable, the average relative light intensity of different height layers was linear regressed, and the light distribution model of fruit tree canopy was obtained, and the model was verified. The results showed that the calibration decision coefficient R2c, root mean square error (RMSEC), validation decision coefficient (R2v), root mean square error (RMSEP) and relative analysis error (RPD) of the established model were 0.924, 0.05, 0.955, 0.04 and 4.91 respectively. The model has high prediction accuracy and strong prediction ability.
【作者單位】： 中國(guó)農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院/現(xiàn)代精細(xì)農(nóng)業(yè)系統(tǒng)集成研究教育部重點(diǎn)試驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(31371532)
【分類(lèi)號(hào)】：S661.1;TP391.41
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本文編號(hào)：2280117