Precision Agriculture Monitoring Using Remote Sensing

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8145

Special Issue Editors


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Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China
Interests: agriculture remote sensing; spatio-temporal data fusion; time series analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Geography and Environment, University of Western Ontario, London, ON N6A5C2, Canada
Interests: algorithms for automatic linear and other man-made feature detection from images; methods for GIS feature extraction and lane use/cover change detection in urban environment using multispectral and hyperspectral data; methods for object oriented information extraction from high resolution remotely sensed imagery; applications of radar/optical remote sensing and GIS for environmental change analysis near large rivers/mountains and in marsh and mangrove wetlands
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture aims at gathering, processing, and analyzing spatiotemporal data in soil (e.g., soil texture and soil moisture) and crop variables related to crop health and dynamics (e.g., leaf area index and leaf chlorophyll content, and water and nitrogen stress) to obtain site-specific crop management strategies for improving resource use efficiency, reducing environmental effects, and maximizing crop productivity. Early crop mapping or real-time monitoring of crop growth is demanded in precision agriculture.  

The rise of near real-time (NRT) collected data from multiple remote sensing platforms, such as satellites, unmanned aerial vehicles, and proximal sensors, in the farmland enable us to conduct NRT agricultural monitoring at a higher spatial resolution. In addition, multi-modal remote sensing sources, such as optical, SAR, and LiDAR data, can provide different features (e.g., red-edge, solar-induced chlorophyll fluorescence, and coherence) to retrieve agricultural variables. In combination with model-data fusion approaches, machine learning techniques, and high-performance computers, advanced approaches of using various remote sensing can be developed for obtaining new insights in precision agriculture. 

This Special Issue focuses on novel methods and applications for precision agricultural monitoring using multi-source remote sensing data. Research areas may include, but are not limited to, the following:

(1) Crop variables (LAI, biomass, height, chlorophyll, and nitrogen) estimation;

(2) Crop phenology detection;

(3) Crop stress (water, nutrient, etc.) identification;

(4) Crop yield prediction;

(5) Crop type mapping;

(6) Site-management zone delineation;

(7) Soil property mapping;

(8) Multi-source, multi-modal data fusion.

Dr. Chunhua Liao
Dr. Taifeng Dong
Prof. Dr. Jiali Shang
Prof. Dr. Jinfei Wang
Guest Editors

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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • crop phenology
  • crop variables
  • yield prediction
  • data fusion
  • time-series analysis
  • sub-field scale
  • near real-time
  • early crop mapping

Published Papers (4 papers)

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Research

22 pages, 5684 KiB  
Article
Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data
by Jiying Kong, Zhenhai Luo, Chao Zhang, Min Tang, Rui Liu, Ziang Xie and Shaoyuan Feng
Agronomy 2023, 13(8), 2147; https://doi.org/10.3390/agronomy13082147 - 16 Aug 2023
Cited by 1 | Viewed by 1060
Abstract
The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices [...] Read more.
The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage. Full article
(This article belongs to the Special Issue Precision Agriculture Monitoring Using Remote Sensing)
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21 pages, 5856 KiB  
Article
Evaluation of Hyperspectral Monitoring Model for Aboveground Dry Biomass of Winter Wheat by Using Multiple Factors
by Chenbo Yang, Jing Xu, Meichen Feng, Juan Bai, Hui Sun, Lifang Song, Chao Wang, Wude Yang, Lujie Xiao, Meijun Zhang and Xiaoyan Song
Agronomy 2023, 13(4), 983; https://doi.org/10.3390/agronomy13040983 - 26 Mar 2023
Cited by 1 | Viewed by 1433
Abstract
The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time [...] Read more.
The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R2c, RMSEc, R2v, RMSEv, and RPD are 0.9487, 0.1663 kg·m−2, 0.7335, 0.3600 kg·m−2, and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology. Full article
(This article belongs to the Special Issue Precision Agriculture Monitoring Using Remote Sensing)
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8 pages, 3261 KiB  
Communication
Short Communication: Spatial Dependence Analysis as a Tool to Detect the Hidden Heterogeneity in a Kenaf Field
by Gyujin Jang, Dong-Wook Kim, Hak-Jin Kim and Yong Suk Chung
Agronomy 2023, 13(2), 428; https://doi.org/10.3390/agronomy13020428 - 31 Jan 2023
Cited by 2 | Viewed by 1141
Abstract
Ever since research attention was first paid to phenomics, it has mainly focused on the use of high throughput phenotyping for characterizing traits in an accurate and fast manner. It was recently realized that its use has huge potential in precision agriculture. However, [...] Read more.
Ever since research attention was first paid to phenomics, it has mainly focused on the use of high throughput phenotyping for characterizing traits in an accurate and fast manner. It was recently realized that its use has huge potential in precision agriculture. However, the focus so far has mainly been on ”obtain large data set”, not on “how to analyze them”. Here, the expanded application of high throughput phenotyping combined with special dependence analysis is demonstrated to reveal the hidden field heterogeneity, using a kenaf field. Based on the method used in the study, the results showed that the growth of kenaf in the field was grouped into two, which led to a large variation of sources among replications. This method has potential to be applied to detect hidden heterogeneity, to be utilized and applied in plant breeding not only for better analysis, but also for better management of fields in precision agriculture. Full article
(This article belongs to the Special Issue Precision Agriculture Monitoring Using Remote Sensing)
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19 pages, 4117 KiB  
Article
Development Trends in Precision Agriculture and Its Management in China Based on Data Visualization
by Chuanhong Song, Wenbo Ma, Junjie Li, Baoshan Qi and Bangfan Liu
Agronomy 2022, 12(11), 2905; https://doi.org/10.3390/agronomy12112905 - 21 Nov 2022
Cited by 5 | Viewed by 3400
Abstract
Recent innovations are increasingly recognizing applications in precision agricultural systems that use data science techniques as well as so-called machine learning techniques. Big data analytics have created various data-intensive decision-making opportunities. This study reviews the big data analysis practices in the agriculture industry [...] Read more.
Recent innovations are increasingly recognizing applications in precision agricultural systems that use data science techniques as well as so-called machine learning techniques. Big data analytics have created various data-intensive decision-making opportunities. This study reviews the big data analysis practices in the agriculture industry to resolve various problems to provide prospects and exciting fields of application in China. In the successful implementation of precise farming, the high-volume and complicated data generated present challenges for the economic growth of China. Emerging deep learning techniques seem promising and must be reinvented to meet current challenges. Thus, this paper suggests a big data analytics agriculture monitoring system (BDA-AMS) to ensure the highly accurate prediction of crop yield in precision agriculture and economic management using a deep learning algorithm. The convolution neural network gathers the raw images from UAVs and performs early predictions of crop yield. The simulation analysis using an open-source agricultural dataset resulted in a high parameter–precision ratio (98.8%), high accuracy (98.9%), a better performance ratio (95.5%), an improved data transmission rate (97.8%), a reduced power consumption ratio (18.8%), and an enhanced weather forecasting ratio (94.8%), production density ratio (98.8%), and reliability ratio (98.6%) compared to the baseline models. Full article
(This article belongs to the Special Issue Precision Agriculture Monitoring Using Remote Sensing)
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