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Remote Sensing
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Proximal and Remote Sensing for Precision Crop Management II
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Proximal and Remote Sensing for Precision Crop Management II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6431

Special Issue Editors

Dr. Yuxin Miao

E-Mail Website
Guest Editor
Precision Agriculture Center, University of Minnesota, St. Paul, MN 55108, USA
Interests: precision agriculture; proximal and remote sensing; precision nitrogen management; integration of crop growth modeling; remote sensing and machine/deep learning; integrated precision crop management; food security and sustainable development
Special Issues, Collections and Topics in MDPI journals
Dr. Yanbo Huang

E-Mail Website
Guest Editor
Genetics and Sustainable Agriculture Research Unit, U.S. Department of Agriculture–Agricultural Research Service, Mississippi State, MS 39762, USA
Interests: process modeling; optimization and control; remote sensing; artificial intelligence; machine learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Ce Yang

E-Mail Website
Guest Editor
Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Interests: application of advanced ideas of robotics; remote sensing; data mining and information technology in precision agriculture; multispectral/hyperspectral imaging; spectroscopy; machine learning; geographic information system (GIS); digital mapping; biochemical sensing; phenotyping
Special Issues, Collections and Topics in MDPI journals
Dr. Krzysztof Kusnierek

E-Mail Website
Guest Editor
Center for Precision Agriculture, Department of Agricultural Technology and System Analysis, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226, 2849 Kapp, Norway
Interests: precision agriculture; site-specific fertilization; agricultural technology; remote sensing; crop spectroscopy; crop water and nutrient stress; soil spectroscopy and mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Xin (Rex) Sun

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58105, USA
Interests: precision agriculture; artificial intelligence; robotics; automation and remote sensing in agriculture; technologies for improving crop; livestock and food production
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Cao

E-Mail Website
Guest Editor
National Engineering and Technology Center for Information Agriculture, Department of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: precision nitrogen/water management; soil management zone; remote-sensing-based nitrogen status diagnosis; precision crop management; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the most significant challenges of the 21st century is how to simultaneously increase crop yield and resource use efficiency, to achieve food security and sustainable agricultural development while protecting the environment in response to global climate change. Precision agriculture has the potential to make significant contributions to meet this challenge. Precision agriculture research has mainly focused on the precision management of different components of crop production, such as nutrient, water, weed, disease, harvest, tillage, etc. These precision management technologies can significantly improve resource-use efficiency, but often have a limited impact on crop yield, which is influenced by genetics, environmental factors and management practices, as well as their interactions. Therefore, precision agriculture must move from the management of a single practice or input to integrated precision crop management systems, to improve crop yield, quality, profitability, and sustainability.

Proximal and remote sensing technologies are crucially important to the development of successful precision crop management strategies and systems. This Special Issue aims to help readers keep up with progress on the applications of proximal crop sensors, both manned and unmanned airborne remote sensing, and high-spatial- and temporal-resolution satellite remote sensing, looking at different aspects of the precision management of cereal crops, vegetables, fruit trees, etc., as well as the development of integrated precision crop management systems with intelligent and smart operations. We would like to invite you to submit research and review papers on (but not limited to) the following topics:

  • Proximal and remote-sensing-based non-destructive detection of plant nutrient stress, water stress, weed, disease, insects, etc;
  • Simultaneous diagnosis of different crop stress factors;
  • Applications of proximal and remote sensing for soil mapping and crop yield and quality assessment;
  • Remote-sensing-based, site-specific management zone delineation for precision crop management;
  • Proximal and remote-sensing-based precision crop management strategies, including the management of nitrogen and other nutrients, seeding, tillage, weed, disease, insects, and lodging;
  • Combining remote sensing, machine learning, and crop growth modeling for precision crop management;
  • Data fusion of plant health sensing and other related information for precision crop management;
  • Sensing-technology-based integrated precision crop management systems;
  • New sensing technologies for precision crop management;
  • Applications of artificial intelligence, machine/deep learning, and big data analysis for precision crop management.

Dr. Yuxin Miao
Dr. Yanbo Huang
Dr. Ce Yang
Dr. Krzysztof Kusnierek
Dr. Xin (Rex) Sun
Dr. Qiang Cao
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. Remote Sensing 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

  • proximal sensing
  • airborne and satellite remote sensing
  • crop stress diagnosis
  • soil mapping
  • precision crop management
  • artificial intelligence
  • machine/deep learning
  • big data
  • integrated sensing
  • data fusion and analytics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

22 pages, 14264 KiB  
Article
Construction and Application of Dynamic Threshold Model for Agricultural Drought Grades Based on Near-Infrared and Short-Wave Infrared Bands for Spring Maize
by Xia Wu, Peijuan Wang, Yanduo Gong, Yuanda Zhang, Qi Wang, Yang Li, Jianping Guo and Shuxin Han
Remote Sens. 2024, 16(17), 3260; https://doi.org/10.3390/rs16173260 - 3 Sep 2024
Viewed by 407
Abstract
Maize (Zea mays L.) is one of the most important grain crops in the world. Drought caused by climate change in recent years may greatly threaten water supply and crop production, even if the drought only lasts for a few days or [...] Read more.
Maize (Zea mays L.) is one of the most important grain crops in the world. Drought caused by climate change in recent years may greatly threaten water supply and crop production, even if the drought only lasts for a few days or weeks. Therefore, effective daily drought monitoring for maize is crucial for ensuring food security. A pivotal challenge in current related research may be the selection of data collection and the methodologies in the construction of these indices. Therefore, orthorectified reflectance in the short-wave infrared (SWIR) band, which is highly sensitive to variations in vegetation water content, was daily obtained from the MODIS MCD43A4 product. Normalized Difference Water Index (NDWI) calculated using the NIR and SWIR bands and days after planting (DAP) were normalized to obtain the Vegetation Water Index (VWI) and normalized days after planting (NDAP), respectively. The daily dynamic threshold model for different agricultural drought grades was constructed based on the VWI and NDAP with double-logistic fitting functions during the maize growing season, and its specific threshold was determined with historical drought records. Verification results indicated that the VWI had a good effect on the daily agricultural drought monitoring of spring maize in the “Golden Maize Belt” in northeast China. Drought grades produced by the VWI were completely consistent with historical records for 84.6% of the validation records, and 96.2% of the validation records differed by only one grade level or less. The VWI can not only daily identify the occurrence and development process of drought, but also well reflect the impact of drought on the yield of maize. Moreover, the VWI could be used to monitor the spatial evolution of drought processes at both regional and precise pixel scales. These results contribute to providing theoretical guidance for the daily dynamic monitoring and evaluation of spring maize drought in the “Golden Maize Belt” of China. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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18 pages, 5377 KiB  
Article
Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics
by Shiji Li, Jianxi Huang, Guilong Xiao, Hai Huang, Zhigang Sun and Xuecao Li
Remote Sens. 2024, 16(17), 3217; https://doi.org/10.3390/rs16173217 - 30 Aug 2024
Viewed by 500
Abstract
Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal [...] Read more.
Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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21 pages, 6182 KiB  
Article
Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging
by Alireza Sanaeifar, Ce Yang, An Min, Colin R. Jones, Thomas E. Michaels, Quinton J. Krueger, Robert Barnes and Toby J. Velte
Remote Sens. 2024, 16(1), 187; https://doi.org/10.3390/rs16010187 - 2 Jan 2024
Cited by 1 | Viewed by 2436
Abstract
Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars ( [...] Read more.
Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380–1022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology’s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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19 pages, 3619 KiB  
Article
Precision Nitrogen Fertilization for Opium Poppy Using Combined Proximal and Remote Sensor Data Fusion
by Muhammad Abdul Munnaf, Angela Guerrero, Maria Calera and Abdul Mounem Mouazen
Remote Sens. 2023, 15(23), 5442; https://doi.org/10.3390/rs15235442 - 21 Nov 2023
Cited by 2 | Viewed by 1235
Abstract
Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible [...] Read more.
Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible and near-infrared reflectance spectroscopy was used to estimate soil pH, organic carbon (OC), soil organic matter (SOM), P, K, Mg, Ca, Na, moisture content (MC), Ca:Mg and K:Mg for one field in Spain. Normalized difference vegetation indexes of the previous crop were retrieved from Sentine-2 images. Rasterization of soil and crop data layers created a spatially homogenous dataset followed by delineation of a management zone (MZ) map using a k-means cluster analysis. MZ clusters were ranked relying on the within-cluster soil fertility attributes. A strip experiment was conducted by creating parallel stripes distributed over the MZ map, over which two SNF treatments (i.e., SNF-Kings approach [KA] and SNF-Robin Hood approach [RHA]) were compared against the uniform rate N (URN) control treatment. In SNF-KA, the highest and lowest N dose was applied in the most and least fertile MZ, respectively, whereas the opposite approach was adopted in the SNF-RHA treatment. Yield and cost–benefit analyses provided both SNF treatments to produce more yield (KA = 2.72 and RHA = 2.74 t ha−1) than the URN (2.64 t ha−1) treatment, leading to increasing gross margins by EUR 91 ha−1 (SNF–KA) and EUR 88.5 ha−1 (SNF–RHA). While SNF-KA reduced N input by 66.54 kg N ha−1, SNF–RHA applied more N by 17.90 kg N ha−1 than URN. Additionally, SNF–RHA attempted to equalize yield responses to N across MZ classes, with a small increase in N input. This study, therefore, suggests adopting SNF–RHA for increasing yield and gross margin and accurate distribution of N according to per MZ N response. Future studies, however, should address the limitations of the current study by delineating MZ maps with the incorporation of additional soil information (e.g., mineral N and clay) for optimizing N doses as well as evaluating agroeconomic performance across multiple sites and years using a full-budget analysis. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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