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Agronomy
Special Issues
Remote Sensing Applications in Crop Monitoring and Modelling
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Remote Sensing Applications in Crop Monitoring and Modelling

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 December 2024) | Viewed by 10382

Special Issue Editors

Dr. Minghan Cheng

E-Mail Website
Guest Editor
Agricultural College, Yangzhou University, Yangzhou, 225009, China
Interests: remote sensing in agriculture; machine learning; crop phenotyping; smart agriculture; crop modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chengming Sun

E-Mail Website
Guest Editor
Smart Agriculture Research Institute, Yangzhou University, Yangzhou, 225009, China
Interests: image recognition (computer vision technology) and intelligent monitoring of crop growth; crop growth simulation and its system design; UAV phenotypic monitoring and data analysis; the design and application of agricultural Internet of Things systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate and timely monitoring of crop growth status is essential to smart and sustainable agricultural production. Remote sensing data acquired from various platforms (e.g., satellite, UAV, and ground) have been widely used to capture crop growth status at various spatial and temporal scales. In addition, the development of new sensor technologies provides new insights for crop monitoring. Recently, technologies such as multimodel data fusion, crop model assimilation, machine learning, cloud computing, and computer vision have been studied, pertaining to crop growth monitoring, disaster warning, and yield forecast.

To demonstrate the developments in remote sensing for crop monitoring and modeling, this Special Issue aims to present new and innovative applications of remote sensing data, collected from various platforms and sensors, as well as highlight novel mechanisms and data-driven methods, such as data fusion and artificial intelligence, to tackle the issues facing crop production. Topics of interest include, but are not limited to, the following:

  1. Crop mapping using satellite observations;
  2. Crop growth monitoring using multimodel data fusion;
  3. Cloud computing applications in the remote sensing of agriculture;
  4. High-throughput acquisition of crop phenotypic traits;
  5. Crop biophysical and biochemical parameter retrieval;
  6. Crop yield forecasting;
  7. Crop disaster warning;
  8. Crop model and data assimilation.

Dr. Minghan Cheng
Prof. Dr. Chengming Sun
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
  • crop monitoring and modeling
  • multimodal data fusion
  • crop phenotype
  • machine learning

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Related Special Issue

Published Papers (6 papers)

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Research

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13 pages, 17057 KiB  
Article
Detection of Crop Damage in Maize Using Red–Green–Blue Imagery and LiDAR Data Acquired Using an Unmanned Aerial Vehicle
by Barbara Dobosz, Dariusz Gozdowski, Jerzy Koronczok, Jan Žukovskis and Elżbieta Wójcik-Gront
Agronomy 2025, 15(1), 238; https://doi.org/10.3390/agronomy15010238 - 18 Jan 2025
Viewed by 1169
Abstract
Crop damage caused by wild animals, particularly wild boars (Sus scrofa), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing the Deepness plugin [...] Read more.
Crop damage caused by wild animals, particularly wild boars (Sus scrofa), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing the Deepness plugin in QGIS, utilizing high-resolution RGB imagery; and (2) a method based on digital surface models (DSMs) derived from LiDAR data. Manual visual assessment, supported by ground-truthing, served as the reference for validating these methods. This study was conducted in 2023 in a maize field in Central Poland, where UAV flights captured high-resolution RGB imagery and LiDAR data. Results indicated that the DSM-based method achieved higher accuracy (94.7%) and sensitivity (69.9%) compared to the deep learning method (accuracy: 92.9%, sensitivity: 35.3%), which exhibited higher precision (92.2%) and specificity (99.7%). The DSM-based method provided a closer estimation of the total damaged area (9.45% of the field) compared to the reference (10.50%), while the deep learning method underestimated damage (4.01%). Discrepancies arose from differences in how partially damaged areas were classified; the deep learning approach excluded these zones, focusing on fully damaged areas. The findings suggest that while DSM-based methods are well-suited for quantifying extensive damage, deep learning techniques detect only completely damaged crop areas. Combining these methods could enhance the accuracy and efficiency of crop damage assessments. Future studies should explore integrated approaches across diverse crop types and damage patterns to optimize wild animal damage evaluation. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling)
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21 pages, 44945 KiB  
Article
Grape Target Detection Method in Orchard Environment Based on Improved YOLOv7
by Fuchun Sun, Qiurong Lv, Yuechao Bian, Renwei He, Dong Lv, Leina Gao, Haorong Wu and Xiaoxiao Li
Agronomy 2025, 15(1), 42; https://doi.org/10.3390/agronomy15010042 - 27 Dec 2024
Viewed by 524
Abstract
In response to the poor detection performance of grapes in orchards caused by issues such as leaf occlusion and fruit overlap, this study proposes an improved grape detection method named YOLOv7-MCSF based on the You Only Look Once v7 (YOLOv7) framework. Firstly, the [...] Read more.
In response to the poor detection performance of grapes in orchards caused by issues such as leaf occlusion and fruit overlap, this study proposes an improved grape detection method named YOLOv7-MCSF based on the You Only Look Once v7 (YOLOv7) framework. Firstly, the original backbone network is replaced with MobileOne to achieve a lightweight improvement of the model, thereby reducing the number of parameters. In addition, a Channel Attention (CA) module was added to the neck network to reduce interference from the orchard background and to accelerate the inference speed. Secondly, the SPPFCSPC pyramid pooling is embedded to enhance the speed of image feature fusion while maintaining a consistent receptive field. Finally, the Focal-EIoU loss function is employed to optimize the regression prediction boxes, accelerating their convergence and improving regression accuracy. The experimental results indicate that, compared to the original YOLOv7 model, the YOLOv7-MCSF model achieves a 26.9% reduction in weight, an increase in frame rate of 21.57 f/s, and improvements in precision, recall, and mAP of 2.4%, 1.8%, and 3.5%, respectively. The improved model can efficiently and in real-time identify grape clusters, providing technical support for the deployment of mobile devices and embedded grape detection systems in orchard environments. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling)
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14 pages, 9522 KiB  
Article
Changes in Vegetation Greenness and Responses to Land Use Changes in the Yongding River Basin (in North China) from 2002 to 2022
by Dongming Zhang, Mingxuan Yi, Zhengguo Sun, Yajie Wang and Kelin Sui
Agronomy 2024, 14(10), 2292; https://doi.org/10.3390/agronomy14102292 - 6 Oct 2024
Cited by 1 | Viewed by 829
Abstract
Vegetation is an important component of an ecosystem, fulfilling various ecological functions in areas such as soil and water conservation, climate regulation, and water source maintenance. This study focuses on the Yongding River Basin as a research area. This study used vegetation indices [...] Read more.
Vegetation is an important component of an ecosystem, fulfilling various ecological functions in areas such as soil and water conservation, climate regulation, and water source maintenance. This study focuses on the Yongding River Basin as a research area. This study used vegetation indices with long time series as a data source in combination with Landsat land use data. This study applied linear trend estimation to analyze the interannual variation trend in vegetation greenness from 2002 to 2022 in the Yongding River Basin and quantitatively analyzed the impact of land use changes on vegetation greenness. The results show that, from 2002 to 2022, the vegetation greenness in the Yongding River Basin has shown an overall increasing trend. The average growth season and the maximum annual normalized difference vegetation index (NDVI) growth rates were 0.006/10a and 0.008/10a, respectively, and the area of increased vegetation greenness accounted for 90% of the total area. During the main growth season (April to October) in the Yongding River Basin, the NDVI generally showed a spatial pattern of being higher in mountainous areas and lower in water areas, with the largest coefficient of variation in vegetation in the river water areas, and the most stable vegetation in forest land. In terms of the changes in vegetation greenness, the contribution rate of arable land was between 36.73% and 38.63%, followed by grassland and forest land, with contribution rates of 26.86% to 27.11% and 23.94% to 26.43%, respectively. The total contribution rate of water areas, construction land, and unused land was around 10.18%. This study can provide a theoretical basis for environmental protection and rational land use in the Yongding River Basin. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling)
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17 pages, 4755 KiB  
Article
Research on Rapeseed Above-Ground Biomass Estimation Based on Spectral and LiDAR Data
by Yihan Jiang, Fang Wu, Shaolong Zhu, Weijun Zhang, Fei Wu, Tianle Yang, Guanshuo Yang, Yuanyuan Zhao, Chengming Sun and Tao Liu
Agronomy 2024, 14(8), 1610; https://doi.org/10.3390/agronomy14081610 - 23 Jul 2024
Cited by 2 | Viewed by 1179
Abstract
The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB [...] Read more.
The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB by extracting spectral indices from spectral images. This study aims to construct a model for estimating rapeseed AGB by combining spectral and LiDAR data. This study incorporates LiDAR data into the spectral data to construct a regression model. Models are separately constructed for the overall rapeseed varieties, nitrogen application, and planting density to find the optimal method for estimating rapeseed AGB. The results show that the R² for all samples in the study reached above 0.56, with the highest overall R² being 0.69. The highest R² for QY01 and ZY03 varieties was 0.56 and 0.78, respectively. Under high- and low-nitrogen conditions, the highest R² was 0.64 and 0.67, respectively. At a planting density of 36,000 plants per mu, the highest R² was 0.81. This study has improved the accuracy of estimating rapeseed AGB. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling)
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Review

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40 pages, 6726 KiB  
Review
Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects
by Jun Wang, Yanlong Wang and Zhengyuan Qi
Agronomy 2024, 14(9), 1920; https://doi.org/10.3390/agronomy14091920 - 27 Aug 2024
Cited by 5 | Viewed by 5094
Abstract
The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence [...] Read more.
The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling)
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Other

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38 pages, 130318 KiB  
Project Report
Remote Sensing Applications for Pasture Assessment in Kazakhstan
by Gulnara Kabzhanova, Ranida Arystanova, Anuarbek Bissembayev, Asset Arystanov, Janay Sagin, Beybit Nasiyev and Aisulu Kurmasheva
Agronomy 2025, 15(3), 526; https://doi.org/10.3390/agronomy15030526 - 21 Feb 2025
Viewed by 800
Abstract
Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for [...] Read more.
Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling)
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