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The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition
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The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Protection and Biotic Interactions".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2631

Special Issue Editor

Prof. Dr. Youzhen Xiang

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling 712100, China
Interests: smart irrigation; efficient use of crop water and fertilizer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of spectroscopic techniques in the fields of agriculture and forestry has emerged as a focal point of research. As such, this Special Issue is dedicated to exploring the innovative applications of spectroscopic techniques in these domains, particularly on proximal and remote scales.

The non-invasive nature and high sensitivity of this technology render it an ideal choice for the study of plant ecosystems. Through spectroscopic techniques, we gain profound insights into the physiological status, growth processes, and environmental adaptability of crops and forest vegetation. From monitoring plant health to soil analysis, and from assessing water quality to monitoring forest ecosystems, spectroscopic technology provides a wealth of data, facilitating precision agriculture and sustainable forestry management.

This Special Issue warmly welcomes original research articles, reviews, and brief communications, focusing on the fundamental and applied research of spectroscopic techniques in the analysis and sensing of crop and plant systems. We eagerly anticipate contributions (ranging from laboratory to field settings and from proximal to remote scales), that aim to foster the continuous innovation of spectroscopic technology in the fields of agriculture and forestry.

Prof. Dr. Youzhen Xiang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • thermal infrared imaging
  • unmanned aerial vehicles (UAVs)
  • multispectral
  • hyperspectral
  • plants
  • crops
  • forestry
  • remote sensing
  • precision agriculture

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Published Papers (6 papers)

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Research

22 pages, 4650 KiB  
Article
RGB Indices Can Be Used to Estimate NDVI, PRI, and Fv/Fm in Wheat and Pea Plants Under Soil Drought and Salinization
by Yuriy Zolin, Alyona Popova, Lyubov Yudina, Kseniya Grebneva, Karina Abasheva, Vladimir Sukhov and Ekaterina Sukhova
Plants 2025, 14(9), 1284; https://doi.org/10.3390/plants14091284 - 23 Apr 2025
Viewed by 217
Abstract
Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal [...] Read more.
Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors. Potentially, RGB indices can be used to estimate narrow-band reflectance indices and/or photosynthetic parameters in plants. Analysis of this problem was the main task of the current work. We investigated relationships of six RGB indices (r, g, b, ExG, VEG, and VARI) to widely used narrow-band reflectance indices (the normalized difference vegetation index, NDVI, and photochemical reflectance index, PRI) and the potential quantum yield of photosystem II (Fv/Fm) in wheat and pea plants under soil drought and salinization. It was shown that investigated RGB indices, NDVI, PRI, and Fv/Fm were significantly changed under the action of both stressors; changes in some RGB indices (e.g., ExG) were initiated on the early stage of action of drought or salinization. Correlation analysis showed that RGB indices (especially, ExG, VARY, and g) were strongly related to the NDVI, PRI, and Fv/Fm; linear regressions between these values were calculated. It means that RGB indices measured by simple and low-cost color cameras can be used to estimate plant parameters (NDVI, PRI, and Fv/Fm) requiring sophisticated equipment to measure. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition)
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19 pages, 6455 KiB  
Article
Winter Oilseed Rape LAI Inversion via Multi-Source UAV Fusion: A Three-Dimensional Texture and Machine Learning Approach
by Zijun Tang, Junsheng Lu, Ahmed Elsayed Abdelghany, Penghai Su, Ming Jin, Siqi Li, Tao Sun, Youzhen Xiang, Zhijun Li and Fucang Zhang
Plants 2025, 14(8), 1245; https://doi.org/10.3390/plants14081245 - 19 Apr 2025
Viewed by 198
Abstract
Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices and texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise for LAI estimation, the limitations [...] Read more.
Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices and texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise for LAI estimation, the limitations of single-texture features necessitate further exploration. Therefore, this study conducted field experiments over two consecutive years (2021–2022) to collect winter oilseed rape LAI ground truth data and corresponding UAV multispectral imagery. Vegetation indices were constructed, and canopy texture features were extracted. Subsequently, a correlation matrix method was employed to establish novel randomized combinations of three-dimensional texture indices. By analyzing the correlations between these parameters and winter oilseed rape LAI, variables with significant correlations (p < 0.05) were selected as model inputs. These variables were then partitioned into distinct combinations and input into three machine learning models—Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), and Extreme Gradient Boosting (XGBoost)—to estimate winter oilseed rape LAI. The results demonstrated that the majority of vegetation indices and texture features exhibited significant correlations with LAI (p < 0.05). All randomized texture index combinations also showed strong correlations with LAI (p < 0.05). Notably, the three-dimensional texture index NDTTI exhibited the highest correlation with LAI (R = 0.725), derived from the spatial combination of DIS5, VAR5, and VAR3. Integrating vegetation indices, texture features, and three-dimensional texture indices as inputs into the XGBoost model yielded the highest estimation accuracy. The validation set achieved a determination coefficient (R2) of 0.882, a root mean square error (RMSE) of 0.204 cm2cm−2, and a mean relative error (MRE) of 6.498%. This study provides an effective methodology for UAV-based multispectral monitoring of winter oilseed rape LAI and offers scientific and technical support for precision agriculture management practices. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition)
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24 pages, 27624 KiB  
Article
Growth Trend Prediction and Intervention of Panax Notoginseng Growth Status Based on a Data-Driven Approach
by Jiahui Ye, Xiufeng Zhang, Gengen Li, Chunxi Yang, Qiliang Yang and Yuzhe Shi
Plants 2025, 14(8), 1226; https://doi.org/10.3390/plants14081226 - 16 Apr 2025
Viewed by 186
Abstract
In crop growth, irrigation has to be adjusted according to developmental stages. Smart agriculture requires the accurate prediction of growth status and timely intervention to improve the quality of agricultural products, but this task faces significant challenges due to variable environmental factors. To [...] Read more.
In crop growth, irrigation has to be adjusted according to developmental stages. Smart agriculture requires the accurate prediction of growth status and timely intervention to improve the quality of agricultural products, but this task faces significant challenges due to variable environmental factors. To address this issue, this study proposes a data-driven irrigation method to enhance crop yield. Our approach harvests extensive datasets to train and optimize an integrated deep-learning architecture combining Informer, Long Short-Term Memory (LSTM) networks, and Exponential Weighted Moving Average (EWMA) models. Controlled greenhouse experiments validated the reliability and practicality of the proposed prediction and intervention strategy. The results showed that the model accurately issued irrigation warnings 3–5 days in advance. Compared to traditional fixed irrigation, the model significantly reduced irrigation frequency while maintaining the same or even better growth conditions. In terms of plant quantity, the experimental group increased by 410.0%, while the control group grew by 50.0%. Additionally, the experimental group’s average plant height was 21.8% higher than that of the control group. These results demonstrate the efficacy of the proposed irrigation prediction method in enhancing crop growth and yield, providing a novel strategy for future agricultural planning and management. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition)
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14 pages, 2104 KiB  
Article
Rice Quality and Yield Prediction Based on Multi-Source Indicators at Different Periods
by Yufei Hou, Huiyu Bao, Tamanna Islam Rimi, Siyuan Zhang, Bangdong Han, Yizhuo Wang, Ziyang Yu, Jianxin Chen, Hongxiu Gao, Zhenqing Zhao, Qiaorong Wei, Qingshan Chen and Zhongchen Zhang
Plants 2025, 14(3), 424; https://doi.org/10.3390/plants14030424 - 1 Feb 2025
Cited by 1 | Viewed by 775
Abstract
This study aims to develop an effective and reliable method for estimating rice quality indices and yield, addressing the growing need for rapid, non-destructive, and accurate predictions in modern agriculture. Field experiments were conducted in 2018 at the Suiling Water Conservancy Comprehensive Experimental [...] Read more.
This study aims to develop an effective and reliable method for estimating rice quality indices and yield, addressing the growing need for rapid, non-destructive, and accurate predictions in modern agriculture. Field experiments were conducted in 2018 at the Suiling Water Conservancy Comprehensive Experimental Station (47°27′ N, 127°06′ E), using Longqingdao 3 as the test variety. Measurements included the leaf area index (LAI), chlorophyll content (SPAD), leaf nitrogen content (LNC), and leaf spectral reflectance during the tillering, jointing, and maturity stages. Based on these parameters, spectral indicators were calculated, and univariate linear regression models were developed to predict key rice quality indices. The results demonstrated that the optimal R2 values for brown rice rate, moisture content, and taste value were 0.866, 0.913, and 0.651, with corresponding RMSE values of 0.122, 0.081, and 1.167. After optimizing the models, the R2 values for the brown rice rate and taste value improved significantly to 0.95 (RMSE: 0.075) and 0.992 (RMSE: 0.179), respectively. Notably, the spectral index GM2 during the jointing stage achieved the highest accuracy for yield prediction, with an R2 value of 0.822. These findings confirm that integrating multiple indicators across different growth periods enhances the accuracy of rice quality and yield predictions, offering a robust and intelligent solution for practical agricultural applications. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition)
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18 pages, 3904 KiB  
Article
Correlation Study Between Canopy Temperature (CT) and Wheat Yield and Quality Based on Infrared Imaging Camera
by Yan Yu, Chenyang Li, Wei Shen, Li Yan, Xin Zheng, Zhixiang Yao, Shuaikang Cui, Chao Cui, Yingang Hu and Mingming Yang
Plants 2025, 14(3), 411; https://doi.org/10.3390/plants14030411 - 30 Jan 2025
Cited by 1 | Viewed by 667
Abstract
As an important physiological indicator, wheat canopy temperature (CT) can be observed after flowering in an attempt to predict wheat yield and quality. However, the relationship between CT and wheat yield and quality is not clear. In this study, the CT, photosynthetic rate [...] Read more.
As an important physiological indicator, wheat canopy temperature (CT) can be observed after flowering in an attempt to predict wheat yield and quality. However, the relationship between CT and wheat yield and quality is not clear. In this study, the CT, photosynthetic rate (Pn), filling rate, wheat yield, and wheat quality of 68 wheat lines were measured, in an attempt to establish a connection between CT and yield and quality and accelerate the selection of new varieties. This experiment used an infrared imaging camera to measure the CT of wheat materials planted in the field in 2022. Twenty materials with significant temperature differences were selected for planting in 2023. By comparing the temperature trends in 2022 and 2023, it is believed that materials 4 and 13 were cold-type materials, while materials 3 and 11 were warm-type materials. The main grain filling period of cold-type materials occurs in the middle and late stages of the grain filling period and the Pn and the thousand-grain weights of cold-type materials were higher than those of warm-type materials. Similarly, under continuous rainy conditions, cold-type materials had a higher protein and wet gluten contents, while warm-type materials had higher sedimentation values and shorter formation times. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition)
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22 pages, 9583 KiB  
Article
Broadband Normalized Difference Reflectance Indices and the Normalized Red–Green Index as a Measure of Drought in Wheat and Pea Plants
by Ekaterina Sukhova, Yuriy Zolin, Alyona Popova, Kseniya Grebneva, Lyubov Yudina and Vladimir Sukhov
Plants 2025, 14(1), 71; https://doi.org/10.3390/plants14010071 - 29 Dec 2024
Cited by 1 | Viewed by 596
Abstract
Global climatic changes increase areas that are influenced by drought. Remote sensing based on the spectral characteristics of reflected light is widely used to detect the action of stressors (including drought) in plants. The development of methods of improving remote sensing is an [...] Read more.
Global climatic changes increase areas that are influenced by drought. Remote sensing based on the spectral characteristics of reflected light is widely used to detect the action of stressors (including drought) in plants. The development of methods of improving remote sensing is an important applied task for plant cultivation. Particularly, this improvement can be based on the calculation of reflectance indices and revealing the optimal spectral bandwidths for this calculation. In the current work, we analyzed the sensitivity of broadband-normalized difference reflectance indices and RGB indices to the action of soil drought on pea and wheat plants. Analysis of the heat maps of significant changes in reflectance indices showed that increasing the spectral bandwidths did not decrease this significance in some cases. Particularly, the index RI(659, 553) based on the red and green bandwidths was strongly sensitive to drought action in plants. The normalized red–green index (NRGI), which was the RGB-analog of RI(659, 553) measured by a color camera, was also sensitive to drought. RI(659, 553) and NRGI were strongly related. The results showed that broadband and RGB indices can be used to detect drought action in plants. Full article
(This article belongs to the Special Issue The Application of Spectral Techniques in Agriculture and Forestry—2nd Edition)
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