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Application of Remote Sensing in Crop Production and Farmland Soil...
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Application of Remote Sensing in Crop Production and Farmland Soil Monitoring

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 29 November 2024 | Viewed by 1766

Special Issue Editors

Dr. Jianjun Wang

E-Mail Website
Guest Editor
Agricultural College, Yangzhou University, Yangzhou 225009, China
Interests: precision agriculture; remote sensing; crop growth monitoring; yield estimation and prediction; farmland soil monitoring; synthetic aperture radar; machine learning; environment
Prof. Dr. Jiahua Zhang

E-Mail Website
Guest Editor
Chinese Academy of Sciences (CAS), Beijing 100094, China
Interests: remote sensing of ecosystems; carbon and water cycle modelling; ecological investigation; land-use and -cover changes; vegetation dynamic; climate change and natural disasters
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rafia Mumtaz

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Interests: image processing; satellite remote sensing; GIS; machine learning; computer vision
Dr. Minfeng Xing

E-Mail Website
Guest Editor
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: the synthetic aperture radar image processing; the application of unmanned aerial vehicle; quantitative estimation of land surface variables from satellite remote sensing and on integration of multiple data sources with numerical models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the integration of remote sensing technologies with agricultural practices has ushered in a new era of precision farming and soil management. The capacity to acquire high-resolution data from satellites, UAVs, and other airborne platforms has profoundly transformed the methodologies by which farmers monitor crop growth and assess soil condition. Accurate and timely information on crop growth and soil conditions is essential to precision farming and sustainable agricultural production. Precise and timely insights into crop growth and soil conditions are indispensable for facilitating precision farming and fostering sustainable agricultural production. This Special Issue aims to illuminate the latest advancements in remote sensing techniques and their applications in enhancing crop productivity and monitoring soil conditions directly related to crop growth.

We welcome contributions that delve into various aspects of remote sensing in agriculture, focusing particularly on crop growth monitoring and soil conditions crucial for crop growth. These include, but are not limited to, the following:

  • Utilization of satellite and aerial remote sensing techniques for continuous monitoring of crop development and estimation of yield.
  • Exploration of hyperspectral and multispectral imaging for early detection of crop diseases, nutrient deficiencies, and stress conditions.
  • Utilizing remote sensing enables precise and cost-effective data collection in agricultural fields, facilitating spatial analysis of crop growth patterns and soil variability.
  • Implementation of machine learning and artificial intelligence algorithms to analyze remote sensing data for prediction modeling in different farming practices.
  • Development of innovative models for real-time monitoring of soil moisture, salinity, nutrient content, and other essential soil parameters directly impacting crop health and productivity.
  • Integration of remote sensing data with agricultural models for improved crop management.

Dr. Jianjun Wang
Prof. Dr. Jiahua Zhang
Prof. Dr. Rafia Mumtaz
Dr. Minfeng Xing
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. 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

  • precision agriculture
  • remote sensing
  • crop growth monitoring
  • yield estimation and prediction
  • farmland soil monitoring
  • machine learning
  • environment
  • image processing
  • data fusion

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Published Papers (2 papers)

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Research

26 pages, 19104 KiB  
Article
Accurately Segmenting/Mapping Tobacco Seedlings Using UAV RGB Images Collected from Different Geomorphic Zones and Different Semantic Segmentation Models
by Qianxia Li, Zhongfa Zhou, Yuzhu Qian, Lihui Yan, Denghong Huang, Yue Yang and Yining Luo
Plants 2024, 13(22), 3186; https://doi.org/10.3390/plants13223186 - 13 Nov 2024
Viewed by 335
Abstract
The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment [...] Read more.
The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Crop Production and Farmland Soil Monitoring)
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22 pages, 4942 KiB  
Article
Enhancing Winter Wheat Soil–Plant Analysis Development Value Prediction through Evaluating Unmanned Aerial Vehicle Flight Altitudes, Predictor Variable Combinations, and Machine Learning Algorithms
by Jianjun Wang, Quan Yin, Lige Cao, Yuting Zhang, Weilong Li, Weiling Wang, Guisheng Zhou and Zhongyang Huo
Plants 2024, 13(14), 1926; https://doi.org/10.3390/plants13141926 - 12 Jul 2024
Cited by 1 | Viewed by 783
Abstract
Monitoring winter wheat Soil–Plant Analysis Development (SPAD) values using Unmanned Aerial Vehicles (UAVs) is an effective and non-destructive method. However, predicting SPAD values during the booting stage is less accurate than other growth stages. Existing research on UAV-based SPAD value prediction has mainly [...] Read more.
Monitoring winter wheat Soil–Plant Analysis Development (SPAD) values using Unmanned Aerial Vehicles (UAVs) is an effective and non-destructive method. However, predicting SPAD values during the booting stage is less accurate than other growth stages. Existing research on UAV-based SPAD value prediction has mainly focused on low-altitude flights of 10–30 m, neglecting the potential benefits of higher-altitude flights. The study evaluates predictions of winter wheat SPAD values during the booting stage using Vegetation Indices (VIs) from UAV images at five different altitudes (i.e., 20, 40, 60, 80, 100, and 120 m, respectively, using a DJI P4-Multispectral UAV as an example, with a resolution from 1.06 to 6.35 cm/pixel). Additionally, we compare the predictive performance using various predictor variables (VIs, Texture Indices (TIs), Discrete Wavelet Transform (DWT)) individually and in combination. Four machine learning algorithms (Ridge, Random Forest, Support Vector Regression, and Back Propagation Neural Network) are employed. The results demonstrate a comparable prediction performance between using UAV images at 120 m (with a resolution of 6.35 cm/pixel) and using the images at 20 m (with a resolution of 1.06 cm/pixel). This finding significantly improves the efficiency of UAV monitoring since flying UAVs at higher altitudes results in greater coverage, thus reducing the time needed for scouting when using the same heading overlap and side overlap rates. The overall trend in prediction accuracy is as follows: VIs + TIs + DWT > VIs + TIs > VIs + DWT > TIs + DWT > TIs > VIs > DWT. The VIs + TIs + DWT set obtains frequency information (DWT), compensating for the limitations of the VIs + TIs set. This study enhances the effectiveness of using UAVs in agricultural research and practices. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Crop Production and Farmland Soil Monitoring)
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