A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle

Yunpeng Yuan, Guoxiang Sun (), Guangyu Chen, Qihua Zhang and Lingwei Liang
Additional contact information
Yunpeng Yuan: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Guoxiang Sun: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Guangyu Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Qihua Zhang: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Lingwei Liang: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China

Agriculture, 2025, vol. 15, issue 3, 1-15

Abstract: The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. Firstly, the preprocessing method of spectral characteristic bands combined with Savitzky-Golay (SG) + Standard Normal Variate (SNV) was determined. Subsequently, all sample data were divided into six groups: N-deficient, K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess. The aforementioned models were then used for classification prediction. The results show that RF and CNN + LSTM models demonstrated good predictive performance. Specifically, RF achieved accuracy rates of 70.14%, 90.81%, 88.59%, and 85.37% in the classification tasks of Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. The CNN + LSTM model achieved accuracy rates of 93.33%, 63.33%, 99.2%, 83.33%, and 98.52% in the classification tasks of K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. Finally, in the Leave-One-Group-Out Validation (LOGOV) for validating the model’s generalisation performance, RF performed better in the N-deficient, K-deficient, and Ca-deficient tasks, achieving diagnostic accuracy rates of 80.19%, 81.43%, and 77.02%, respectively. The CNN + LSTM model showed a diagnostic accuracy rate of 66.72% in the N-excess classification task. The study concludes that, given complete training data, the CNN + LSTM model can effectively diagnose mild nutrient stress (N, K, and Ca) in facility-grown tomatoes in most scenarios.

Keywords: facility-grown tomatoes; near-infrared spectroscopy; time series; deep learning; stress diagnosis (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: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/3/307/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/3/307/ (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:3:p:307-:d:1580584

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 ().