Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards

Cheng, Jinpeng; Yang, Hao; Qi, Jianbo; Han, Shengbo; Sun, Zhendong; Feng, Haikuan; Chen, Riqiang; Zhang, Chengjian; Li, Jingbo; Yang, Guijun

doi:10.1016/j.jag.2023.103367

Cited by 4 publications

(1 citation statement)

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“…Many scholars have conducted extensive and in-depth studies on physicochemical parameters such as chlorophyll content, leaf area index (LAI), tree height, and crown width. Chlorophyll content and LAI are important indicators of photosynthesis in jujube trees and the strength of the crop's ability to intercept light [6][7][8]. The inversion of chlorophyll content and LAI by remote sensing images can be used to make a preliminary judgment of the health status of date palm trees.…”

Section: Introductionmentioning

confidence: 99%

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

Wu,

Zhao,

Yin

et al. 2023

Agriculture

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To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

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Section: Introductionmentioning

confidence: 99%