Prediction of Citrus Leaf Water Content Based on Multi-Preprocessing Fusion and Improved 1-Dimensional Convolutional Neural Network
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| Publicado en: | Horticulturae vol. 11, no. 4 (2025), p. 413 |
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| Autor Principal: | |
| Outros autores: | , , , , , |
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MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3194612435 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2311-7524 | ||
| 024 | 7 | |a 10.3390/horticulturae11040413 |2 doi | |
| 035 | |a 3194612435 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Dou Shiqing |u College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; doushiqing@glut.edu.cn (S.D.); xz_r@glut.edu.cn (X.R.); 1020221955@glut.edu.cn (W.Z.); 2120242211@glut.edu.cn (X.Y.) | |
| 245 | 1 | |a Prediction of Citrus Leaf Water Content Based on Multi-Preprocessing Fusion and Improved 1-Dimensional Convolutional Neural Network | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit the complex information within spectra, this study proposes a novel strategy for estimating citrus LWC by integrating spectral preprocessing combinations with an enhanced deep learning architecture. Utilizing a citrus plantation in Guangxi as the experimental site, 240 leaf samples were collected. Seven preprocessing combinations were constructed based on multiplicative scatter correction (MSC), continuous wavelet transform (CWT), and first derivative (1st D), and a new multichannel network, EDPNet (Ensemble Data Preprocessing Network), was designed for modelling. Furthermore, this study incorporated an attention mechanism within EDPNet, comparing the applicability of SE Block, SAM, and CBAM in integrating spectral combination information. The experiments demonstrated that (1) the triple preprocessing combination (MSC + CWT + 1st D) significantly enhanced model performance, with the prediction set R² reaching 0.8036, a 13.86% improvement over single preprocessing methods, and the RMSE reduced to 2.3835; (2) EDPNet, through its multichannel parallel convolution and shallow structure design, avoids excessive network depth while effectively enhancing predictive performance, achieving a prediction accuracy (R2 = 0.8036) that was 5.58–9.21% higher than that of AlexNet, VGGNet, and LeNet-5, with the RMSE reduced by 9.35–14.65%; and (3) the introduction of the hybrid attention mechanism CBAM further optimized feature weight allocation, increasing the model’s R2 to 0.8430 and reducing the RMSE to 2.1311, with accuracy improvements of 2.08–2.36% over other attention modules (SE, SAM). This study provides a highly efficient and accurate new method for monitoring citrus water content, offering practical significance for intelligent orchard management and optimal resource allocation. | |
| 651 | 4 | |a China | |
| 653 | |a Resource allocation | ||
| 653 | |a Accuracy | ||
| 653 | |a Monitoring methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Modelling | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Leaves | ||
| 653 | |a Water | ||
| 653 | |a Moisture content | ||
| 653 | |a Fruits | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Continuous wavelet transform | ||
| 653 | |a Efficiency | ||
| 653 | |a Water content | ||
| 653 | |a Preprocessing | ||
| 653 | |a Predictions | ||
| 653 | |a Neural networks | ||
| 653 | |a Trees | ||
| 653 | |a Data collection | ||
| 653 | |a Methods | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Ren Xinze |u College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; doushiqing@glut.edu.cn (S.D.); xz_r@glut.edu.cn (X.R.); 1020221955@glut.edu.cn (W.Z.); 2120242211@glut.edu.cn (X.Y.) | |
| 700 | 1 | |a Qi Xiangqian |u School of Resource Engineering, Longyan University, Longyan 364012, China | |
| 700 | 1 | |a Zhang, Wenjie |u College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; doushiqing@glut.edu.cn (S.D.); xz_r@glut.edu.cn (X.R.); 1020221955@glut.edu.cn (W.Z.); 2120242211@glut.edu.cn (X.Y.) | |
| 700 | 1 | |a Mei Zhengmin |u Guangxi Academy of Specialty Crops, Guilin 541004, China; mzm077@126.com (Z.M.); wrongpiano@163.com (Y.S.) | |
| 700 | 1 | |a Song, Yaqin |u Guangxi Academy of Specialty Crops, Guilin 541004, China; mzm077@126.com (Z.M.); wrongpiano@163.com (Y.S.) | |
| 700 | 1 | |a Yang, Xiaoting |u College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China; doushiqing@glut.edu.cn (S.D.); xz_r@glut.edu.cn (X.R.); 1020221955@glut.edu.cn (W.Z.); 2120242211@glut.edu.cn (X.Y.) | |
| 773 | 0 | |t Horticulturae |g vol. 11, no. 4 (2025), p. 413 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194612435/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194612435/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194612435/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |