Model for Inverting the Leaf Area Index of Green Plums by Integrating IoT Environmental Monitoring Data and Leaf Relative Content of Chlorophyll Values

Caili Yu, Haiyang Tong, Daoyi Huang, Jianqiang Lu (), Jiewei Huang, Dejing Zhou and Jiaqi Zheng
Additional contact information
Caili Yu: Center for Intelligent Perception and Internet of Things Research, Shanwei Institute of Technology, Shanwei 516600, China
Haiyang Tong: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Daoyi Huang: Center for Intelligent Perception and Internet of Things Research, Shanwei Institute of Technology, Shanwei 516600, China
Jianqiang Lu: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Jiewei Huang: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Dejing Zhou: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Jiaqi Zheng: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 11, 1-23

Abstract: The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an R 2 of 0.88 and an R M S E of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the R 2 increased by 0.19 and 0.26, respectively, and the R M S E decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums.

Keywords: green plum; LAI; SPAD; environmental feature factors; MDF?ADRS (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/11/2076/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/11/2076/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:11:p:2076-:d:1523792

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().