ArticlePlant phenomics (Washington, D.C.)2022
Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Near-Infrared Spectroscopy Coupled with Chemometrics for Rapid Determination of pH, Moisture and Lycopene Content in Lycopene Liquid Beverages.Foods (Basel, Switzerland) · 2026Article
- Enhancing the detection of LTP through lyophilized protein samples and NIR spectroscopy with explainable deep learning.Scientific reports · 2026Article
- Sensitive Spectral and Temporal-Spatial Characteristic Analysis of Leaf SPAD in Maize Under Variety and Nitrogen Coupling Effects.Food science & nutrition · 2026Article
- Spectral prediction of anthocyanin concentration inFrontiers in plant science · 2026Article
- Optimizing chlorophyll content prediction in tea leaves via spectral transformations and deep learning.BMC plant biology · 2025Article
- Cross-Crop Transferability of Machine Learning Models for Early Stem Rust Detection in Wheat and Barley Using Hyperspectral Imaging.Plants (Basel, Switzerland) · 2025Article
- Article
- A cross-scale transfer learning framework: prediction of SOD activity from leaf microstructure to macroscopic hyperspectral imaging.Plant biotechnology journal · 2025Article
- Classification of different gluten wheat varieties based on hyperspectral preprocessing, feature screening, and machine learning.Food chemistry: X · 2025Article
- Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning.Frontiers in plant science · 2025Article
- A deep learning architecture for leaf water potential prediction inFrontiers in plant science · 2025Article
- Non-destructive estimation of SPAD and biomass inFrontiers in plant science · 2025Article
- Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content inInternational journal of molecular sciences · 2024Article
- A model for inversion of hyperspectral characteristics of phosphate content in mural plaster based on fractional-order differential algorithm.Scientific reports · 2024Article
- Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters.Plants (Basel, Switzerland) · 2024Article
- Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.Food science & nutrition · 2024Article
- Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.Frontiers in nutrition · 2024Article
- Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques.Plant phenomics (Washington, D.C.) · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.