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Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems 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: closed (31 August 2023) | Viewed by 9447

Special Issue Editors

Dr. Carlos Antonio Da Silva Junior

E-Mail Website
Guest Editor
Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78550-000, MT, Brazil
Interests: remote sensing; LULCC; environment; spatial analysis; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Luciano Shiratsuchi

E-Mail Website
Guest Editor
School of Plant, Enviromental and Soil Sciences, Louisiana State University (LSU), Baton Rouge, LA, USA
Interests: precision agriculture; remote sensing; on-farm precision experimentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the evolution of orbital and proximal remote sensing technologies, big data that must be converted to information are being generated in the agricultural sector. These data, when analyzed with machine and deep learning approaches, can be successfully utilized for remote sensing products. The computational power of cloud-based systems and recent advances in farm machinery providing data collection, processing, and analysis open up several opportunities for the development and adoption of new technologies. Large-scale precision experimentation conducted in partnership with commercial farms and using new sensors on UAVs, crop duster airplanes, and satellites, such as radar technologies that allow daily remote data collection under cloudy skies, are exciting and require further investigation. New equipment and sensors are enabling better crop monitoring and land use at a regional scale.

The previous volume of ‘Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems’, was a great success.  This Special Issue of Remote Sensing aims to present publications from collaborators working with a big pool of data and analyzing them using deep and machine learning approaches in precision agriculture and aiming to improve regional-scale remote sensing applications.

Dr. Carlos Antonio Da Silva Junior
Dr. Luciano Shiratsuchi
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

  • precision agriculture
  • active crop canopy sensors
  • on farm precision experimentation
  • monitoring crop areas
  • neural network
  • image processing
  • orbital sensors

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Published Papers (3 papers)

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Review

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25 pages, 2261 KiB  
Review
Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles
by Feng Yu, Qian Zhang, Jun Xiao, Yuntao Ma, Ming Wang, Rupeng Luan, Xin Liu, Yang Ping, Ying Nie, Zhenyu Tao and Hui Zhang
Remote Sens. 2023, 15(12), 2988; https://doi.org/10.3390/rs15122988 - 8 Jun 2023
Cited by 14 | Viewed by 5614
Abstract
The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. [...] Read more.
The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification. Full article
(This article belongs to the Special Issue Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems II)
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Other

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16 pages, 5268 KiB  
Technical Note
Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network
by Jinjie Wang, Jianli Ding, Si Ran, Shaofeng Qin, Bohua Liu and Xiang Li
Remote Sens. 2023, 15(13), 3283; https://doi.org/10.3390/rs15133283 - 26 Jun 2023
Cited by 3 | Viewed by 1459
Abstract
At present, forest and fruit resource surveys are mainly based on ground surveys, and the information technology of the characteristic forest and fruit industries is evidently lagging. The automatic extraction of fruit tree information from massive remote sensing data is critical for the [...] Read more.
At present, forest and fruit resource surveys are mainly based on ground surveys, and the information technology of the characteristic forest and fruit industries is evidently lagging. The automatic extraction of fruit tree information from massive remote sensing data is critical for the healthy development of the forest and fruit industries. However, the complex spatial information and weak spectral information contained in high-resolution images make it difficult to classify fruit trees. In recent years, fully convolutional neural networks (FCNs) have been shown to perform well in the semantic segmentation of remote sensing images because of their end-to-end network structures. In this paper, an end-to-end network model, Multi-Unet, was constructed. As an improved version of the U-Net network structure, this structure adopted multiscale convolution kernels to learn spatial semantic information under different receptive fields. In addition, the “spatial-channel” attention guidance module was introduced to fuse low-level and high-level features to reduce unnecessary semantic features and refine the classification results. The proposed model was tested in a characteristic high-resolution pear tree dataset constructed through field annotation work. The results show that Multi-Unet was the best performer among all models, with classification accuracy, recall, F1, and kappa coefficient of 88.95%, 89.57%, 89.26%, and 88.74%, respectively. This study provides important practical significance for the sustainable development of the characteristic forest fruit industry. Full article
(This article belongs to the Special Issue Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems II)
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16 pages, 6651 KiB  
Technical Note
Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine
by Zhijun Zhen, Shengbo Chen, Tiangang Yin and Jean-Philippe Gastellu-Etchegorry
Remote Sens. 2023, 15(11), 2761; https://doi.org/10.3390/rs15112761 - 25 May 2023
Cited by 8 | Viewed by 1885
Abstract
Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced [...] Read more.
Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites. Full article
(This article belongs to the Special Issue Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems II)
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