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Remote Sensing
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Applications of Deep Learning in Smart Agriculture
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Applications of Deep Learning in Smart Agriculture

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 12486

Special Issue Editors

Prof. Dr. Karem Chokmani

E-Mail Website
Guest Editor
Centre Eau Terre Environnement, INRS, 490 Rue de la Couronne, Québec, QC G1K 9A9, Canada
Interests: remote sensing; precision agriculture; deep learning; geomatics; spatial and temporal variability of water resources; microclimate; UAVs
Special Issues, Collections and Topics in MDPI journals
Dr. Yacine Bouroubi

E-Mail Website
Guest Editor
Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Interests: remote sensing; deep learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Saeid Homayouni

E-Mail Website
Guest Editor
Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC G1K 9A9, Canada
Interests: remote sensing; geomatics; analysis of optical, SAR, and UAV Earth observations through artificial intelligence and machine learning approaches for agro-environmental applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart agriculture, comprising precision agriculture, digital agriculture, and other new concepts in agricultural research and practice, has gained increasing attention in recent years due to the rising importance of sustainable food production and resource management, as well as to the opportunity offered by the emergence of several digital hardware and software technologies. Accordingly, the development of geospatial, information technology, Internet of Things, robotics, artificial intelligence, and data analytics applications plays an essential role in modern farm management. Traditional approaches of information and knowledge collection for the monitoring of agricultural fields is laborious, time-consuming, and may contain uncertainties. Therefore, technological advances in remote sensing platforms and sensors, digital web applications, and cloud data storage and management centers, as well as the development of intelligent data analysis methods and decision support systems, have improved the quality of monitoring of agricultural lands in order to meet agricultural requirements. Smart agriculture, based on today’s variable-rate technology, geospatial technology, sensor technology, Internet of Things, open-source data and algorithms, machine learning (e.g., deep learning), and high-performance computing can benefit from these opportunities and can address the new food production challenges related to cropping system optimization for improving productivity and reducing environmental impacts.

This is a joint Special Issue of Agronomy and Remote Sensing, titled “Applications of Deep Learning in Smart Agriculture,” that aims to present the state-of-the-art and original analytical methods based on deep learning for transforming diverse advanced agro-environmental data from machinery, drone, airborne, and satellite sensors into information relevant to various agronomy applications. Research papers that examine the latest developments in concepts, methods, techniques, and case study applications are welcomed. According to the aims and scope of these journals, articles based on the application of deep learning to agricultural remote sensing data can be submitted to Remote Sensing, while articles presenting analyses of other types of data or technologies in smart/precision agriculture can be submitted to Agronomy.

You may choose our Joint Special Issue in Agronomy.

Dr. Karem Chokmani
Dr. Yacine Bouroubi
Dr. Saeid Homayouni
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

  • smart agriculture
  • digital agriculture
  • precision agriculture
  • variable-rate technology
  • automatic agricultural screening
  • deep learning
  • computer vision
  • convolutional neural networks
  • recurrent neural networks
  • data mining
  • data analytics
  • Big Data
  • modeling
  • remote sensing (satellite, airborne, UAV Imagery, and proximal sensing)
  • crop monitoring and mapping
  • disease detection
  • phenological characterization
  • global positioning system and geospatial information technology
  • Robotics
  • Internet of Things

Published Papers (3 papers)

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Research

23 pages, 8593 KiB  
Article
A Convolutional Neural Network for Large-Scale Greenhouse Extraction from Satellite Images Considering Spatial Features
by Zhengchao Chen, Zhaoming Wu, Jixi Gao, Mingyong Cai, Xuan Yang, Pan Chen and Qingting Li
Remote Sens. 2022, 14(19), 4908; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194908 - 30 Sep 2022
Cited by 2 | Viewed by 1552
Abstract
Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a [...] Read more.
Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network’s ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture)
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14 pages, 9487 KiB  
Article
Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing
by Joby M. Prince Czarnecki, Sathishkumar Samiappan, Meilun Zhou, Cary Daniel McCraine and Louis L. Wasson
Remote Sens. 2021, 13(19), 3859; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193859 - 27 Sep 2021
Cited by 3 | Viewed by 2260
Abstract
The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from [...] Read more.
The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture)
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24 pages, 12674 KiB  
Article
Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images
by François Waldner, Foivos I. Diakogiannis, Kathryn Batchelor, Michael Ciccotosto-Camp, Elizabeth Cooper-Williams, Chris Herrmann, Gonzalo Mata and Andrew Toovey
Remote Sens. 2021, 13(11), 2197; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112197 - 04 Jun 2021
Cited by 32 | Viewed by 7179
Abstract
Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title [...] Read more.
Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title records, field boundaries are not historically recorded. As a result, digital services currently ask their users to manually draw their field, which is time-consuming and creates disincentives. Here, we present a generalized method, hereafter referred to as DECODE (DEtect, COnsolidate, and DElinetate), that automatically extracts accurate field boundary data from satellite imagery using deep learning based on spatial, spectral, and temporal cues. We introduce a new convolutional neural network (FracTAL ResUNet) as well as two uncertainty metrics to characterize the confidence of the field detection and field delineation processes. We finally propose a new methodology to compare and summarize field-based accuracy metrics. To demonstrate the performance and scalability of our method, we extracted fields across the Australian grains zone with a pixel-based accuracy of 0.87 and a field-based accuracy of up to 0.88 depending on the metric. We also trained a model on data from South Africa instead of Australia and found it transferred well to unseen Australian landscapes. We conclude that the accuracy, scalability and transferability of DECODE shows that large-scale field boundary extraction based on deep learning has reached operational maturity. This opens the door to new agricultural services that provide routine, near-real time field-based analytics. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Smart Agriculture)
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