WT-ResNet: A Non-Destructive Method for Determining the Nitrogen, Phosphorus, and Potassium Content of Sugarcane Leaves Based on Leaf Image

Cuimin Sun, Junyang Dou, Biao He, Yuxiang Cai and Chengwu Zou ()
Additional contact information
Cuimin Sun: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Junyang Dou: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Biao He: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Yuxiang Cai: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Chengwu Zou: State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530004, China

Agriculture, 2025, vol. 15, issue 16, 1-18

Abstract: Traditional nutritional diagnosis suffers from inefficiency, high cost, and damage when predicting the nitrogen, phosphorus, and potassium content of sugarcane leaves. Non-destructive nutritional diagnosis of sugarcane leaves based on traditional machine learning and deep learning suffers from poor generalization and lower accuracy. To address these issues, this study proposes a novel convolutional neural network called WT-ResNet. This model incorporates wavelet transform into the residual network structure, enabling effective feature extraction from sugarcane leaf images and facilitating the regression prediction of nitrogen, phosphorus, and potassium content in the leaves. By employing a cascade of decomposition and reconstruction, the wavelet transform extracts multi-scale features, which allows for the capture of different frequency components in images. Through the use of shortcut connections, residual structures facilitate the learning of identity mappings within the model. The results show that by analyzing sugarcane leaf images, our model achieves R 2 values of 0.9420 for nitrogen content prediction, 0.9084 for phosphorus content prediction, and 0.8235 for potassium content prediction. The accuracy rate for nitrogen prediction reaches 88.24% within a 0.5 tolerance, 58.82% for phosphorus prediction within a 0.1 tolerance, and 70.59% for potassium prediction within a 0.5 tolerance. Compared to other algorithms, WT-ResNet demonstrates higher accuracy. This study aims to provide algorithms for non-destructive sugarcane nutritional diagnosis and technical support for precise sugarcane fertilization.

Keywords: sugarcane; leaf; nitrogen; phosphorus; potassium; wavelet transform; prediction; convolutional neural network (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/16/1752/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/16/1752/ (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:15:y:2025:i:16:p:1752-:d:1725577

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 ().