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Agronomy
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Intelligent Remote Sensing in Smart Agriculture: From Early Identification to...
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Intelligent Remote Sensing in Smart Agriculture: From Early Identification to Yield Prediction—A Comprehensive Solution

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2933

Special Issue Editors

Dr. Liang Sun

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: multi-source remote sensing data fusion algorithm and application; crop classification and yield estimation; surface evapotranspiration and crop drought monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Crusiol

E-Mail Website
Guest Editor
Embrapa Soja (National Soybean Research Centre–Brazilian Agricultural Research Corporation), Londrina 86001-970, PR, Brazil
Interests: remote sensing; crop monitoring; soybean; hyperspectral data; multispectral data; thermal data; drought; unmanned aerial vehicle
Special Issues, Collections and Topics in MDPI journals
Dr. Maofang Gao

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: remote sensing; resources and environment monitoring; fire detection; deep learning; geographic information system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to submit papers to the Special Issue of the open access journal "Agronomy" titled "Intelligent Remote Sensing in Smart Agriculture: From Early Identification to Yield Prediction—A Comprehensive Solution."

Intelligent remote sensing technology plays a crucial role in smart agriculture, particularly in areas like early crop identification, health monitoring, and yield prediction. When combined with advanced data analysis methods, these technologies offer more accurate and efficient management approaches for agricultural production. This Special Issue aims to gather research articles that explore and showcase the latest advancements and applications of intelligent remote sensing technology in smart agriculture. The contributions may include, but are not limited to, the following topics:

  1. Integration of Satellite and UAV Data: exploring the integration of satellite sensors, UAVs, and ground observation data in smart agriculture, especially in early crop identification and growth monitoring.
  2. Intelligent Early Warning Systems: researching how to use remote sensing data and intelligent analysis techniques to build early warning systems, improving the management efficiency of crop health and growth conditions.
  3. Smart Agriculture Yield Estimation: analyzing the application of remote sensing technology in monitoring crop biomass, vegetation cover, and growth rate, as well as the development and implementation of yield prediction models using various methods (such as empirical statistical models, crop models, light-use efficiency models, deep learning and machine learning algorithms, etc.).
  4. Resource Management and Environmental Protection: discussing how accurate yield predictions can aid in the effective management of agricultural resources and the reduction of environmental impacts.
  5. Analysis of the Impact of Climate Change: studying the impact of climate change on agricultural crop growth patterns and exploring adaptive agricultural strategies.
  6. Soil Health Monitoring: utilizing remote sensing technology to assess soil quality, providing data support for soil management and improvement.

We look forward to your submissions!

Dr. Liang Sun
Dr. Luis Crusiol
Dr. Maofang Gao
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

  • remote sensing
  • smart agriculture
  • agriculture monitoring
  • identification
  • yield prediction

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Published Papers (3 papers)

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Research

11 pages, 4980 KiB  
Article
Study on Spatiotemporal Characteristics and Influencing Factors of High-Resolution Single-Season Rice
by Yang Han, Peng Zhou, Youyue Wen, Jian Yang, Qingzhou Lv, Jian Wang and Yanan Zhou
Agronomy 2024, 14(10), 2436; https://doi.org/10.3390/agronomy14102436 - 21 Oct 2024
Viewed by 608
Abstract
Single-season rice describes the area under rice cultivation from May–October of the year. Many scholars have used lower-resolution data to study single-season rice in different regions, but using high-precision and high-resolution single-season rice data can reveal new phenomena. This paper uses a long-time-series, [...] Read more.
Single-season rice describes the area under rice cultivation from May–October of the year. Many scholars have used lower-resolution data to study single-season rice in different regions, but using high-precision and high-resolution single-season rice data can reveal new phenomena. This paper uses a long-time-series, high-precision, and high-resolution single-season rice cultivation dataset to conduct an in-depth analysis of the spatial–temporal variability characteristics of single-season rice in Jiangsu Province, China, from 2017 to 2021. It explores the correlation between meteorological factors and greenhouse gasses for single-season rice. It analyzes the driving role of social factors on single-season rice. The results showed that single-season rice was mainly grown in the central and northeastern regions of the study area. The single-season rice cultivation was significantly reduced in 2020 due to the impact of COVID-19. Single-season rice strongly correlates with meteorological factors in time but shows a weak spatial correlation. This is because human factors largely dominate the area under single-season rice cultivation. Methane emissions in the study area are mainly influenced by anthropogenic activities rather than single-season rice. Social factors are essential in controlling single-season rice cultivation in the study area. This study was conducted in Jiangsu Province, China. Still, the methodology and results have important implications for agricultural production and environmental management studies in other regions, and some findings have general applicability. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing in Smart Agriculture: From Early Identification to Yield Prediction—A Comprehensive Solution)
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19 pages, 12139 KiB  
Article
Inversion Modeling of Chlorophyll Fluorescence Parameters in Cotton Canopy via Moisture Data and Spectral Analysis
by Fuqing Li, Caiyun Yin, Zhen Li, Jiaqiang Wang, Long Jiang, Buping Hou and Jing Shi
Agronomy 2024, 14(10), 2190; https://doi.org/10.3390/agronomy14102190 - 24 Sep 2024
Viewed by 521
Abstract
The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation [...] Read more.
The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing in Smart Agriculture: From Early Identification to Yield Prediction—A Comprehensive Solution)
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16 pages, 1163 KiB  
Article
Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates
by Jiaqiang Wang, Caiyun Yin, Weiyang Liu, Wenhao Xia and Songrui Ning
Agronomy 2024, 14(4), 662; https://doi.org/10.3390/agronomy14040662 - 25 Mar 2024
Viewed by 1196
Abstract
Soil salinity affects nutrient uptake by cotton. The cotton bud stage is a very important period in the process of cotton planting and directly affects the yield of cotton. The nutritional status of the bud stage directly affects the reflectance spectra of cotton [...] Read more.
Soil salinity affects nutrient uptake by cotton. The cotton bud stage is a very important period in the process of cotton planting and directly affects the yield of cotton. The nutritional status of the bud stage directly affects the reflectance spectra of cotton canopy leaves. Therefore, it is of great significance to nondestructively monitor the nutritional status of the cotton bud stage on salinized soil via spectroscopic techniques and perform corresponding management measures to improve cotton yield. In this study, potted plants with different nitrogen application rates were set up to obtain the reflection spectral curves of cotton bud stage leaves, analyze their spectral characteristics under different nitrogen application rates, and establish spectral estimation models of chlorophyll density. The results are as follows: in the continuum removal spectrum of the cotton bud stage, the lowest point of the absorption valley near 500 nm shifted to the shortwave direction with an increasing nitrogen application rate. The mean reflectance between 765 and 880 nm was significantly different between nitrogen-stressed and nitrogen-unstressed cotton. The average reflectance of the near-infrared band, the absorption valley depths near 500 nm and 675 nm, the first derivative of the 710 nm reflectance, and the second derivatives of the 690 nm and 730 nm reflectance increased with increasing nitrogen application and chlorophyll density, and significant correlations were observed with the chlorophyll density. These parameters were modeled using support vector regression (SVR) and artificial neural network (ANN) methods, two commonly used algorithms in the field of machine learning. The determination coefficients of the three chlorophyll samples via the ANN models were 0.92, 0.77, and 0.94 for the modeling set and 0.77, 0.69, and 0.77 for the verification set. The ratio of quartile to root-mean-square error (RPIQ) of the ANN model was greater than 2.2, and the ratio of the standard error of the measured value to the standard error of the predicted (SEL/SEP) was close to 1, indicating that the chlorophyll density estimation models built based on the ANN algorithm had robust prediction ability. Our model could accurately estimate the leaf chlorophyll density in the cotton bud stage. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing in Smart Agriculture: From Early Identification to Yield Prediction—A Comprehensive Solution)
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