Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Sustainable Agriculture and Advances of Remote Sensing
share announcement

Sustainable Agriculture and Advances of Remote Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 151482

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Dimitrios S. Paraforos

E-Mail Website
Guest Editor
Institut für Technik – Department of Agricultural Engineering, Hochschule Geisenheim University, Von-Lade-Str. 1, D-65366 Geisenheim, Germany
Interests: agricultural machinery automation; ISOBUS technologies; unmanned ground and aerial vehicles; decentralized and resilient digital farming systems
Special Issues, Collections and Topics in MDPI journals
Dr. Anselme Muzirafuti

E-Mail Website
Guest Editor
Department of Mathematics, Computer Sciences, Physics and Earth Sciences, University of Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Interests: remote sensing; unmanned aerial vehicles (UAVs); image processing; farming by satellite; geographic information system (GIS); applied geophysics; coastal studies; climate change; land use/cover change; anthropogenic impact; landscape planning; engineering geology; ecological studies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giovanni Randazzo

E-Mail Website
Guest Editor
Department of Mathematics, Computer Sciences, Physics and Earth Sciences, University of Messina, Via F. Stagno d’Alcontres, 31, 98166 Messina, Italy
Interests: geography; geomorphology; geology
Special Issues, Collections and Topics in MDPI journals
Dr. Stefania Lanza

E-Mail Website
Guest Editor
GeoloGIS s.r.l., Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Interests: geography; geomorphology; sedimentology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few decades, remote sensing technologies have witnessed tremendous improvements in sensors, platforms, and data analysis. Numerous satellite programs provide free or commercial imageries, new unmanned aerial vehicles equipped with high performing sensors are being developed, and a number of wireless sensors and types of machinery have been introduced. However, the applications of these technologies in agricultural engineering are limited and not well documented. Remote sensing technologies are efficient and cost-effective for crop production, crop detection, and crop monitoring. They are used in precision agriculture, digital farming, automation, and robotics. With the ongoing climate change, remote sensing technologies can be used to obtain information on land use/land cover as well as in subsurface. They can be used for point clouds acquisition and 3D reconstruction of crops. Free imageries provided by Sentinel and Landsat satellites can be used for leaf area estimation, field boundary, and cultivated area determination. Their precision and performance can be improved by using very high spatial resolution commercial satellites or orthophotos acquired by UAVs. In addition, navigation satellite system signals can be used in agricultural machinery control, automation, and robotics.

This Special Issue will focus on the latest advances in remote sensing technology and agricultural engineering. Authors are invited to submit original manuscripts on topics including (but not limited to): 

  • Photogrammetry for crop detection and crop monitoring;
  • Aquaculture and fisheries;
  • New materials for agriculture engineering;
  • Satellite technologies for farming and precision agriculture;
  • Fusion of remote sensing and applied geophysics for subsurface and hydrogeological modelling;
  • Crop 3D imaging and reconstruction;
  • Multispectral and hyperspectral data analysis;
  • Navigation satellite system signals for agricultural machinery control and site-specific applications;
  • Remote sensing time series data analysis for environmental impact and landscape analyses.

Prof. Dr. Dimitrios S. Paraforos
Dr. Anselme Muzirafuti
Prof. Dr. Giovanni Randazzo
Dr. Stefania Lanza
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. Applied Sciences 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 2400 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

  • agricultural engineering
  • remote sensing
  • novel farming and precision agriculture
  • aquaculture and fisheries
  • fertilisation
  • crop detection and monitoring
  • satellite technologies
  • automatic control
  • unmanned aerial vehicles (UAVs)
  • image processing
  • farming by satellite
  • digital farming
  • geographic information system (GIS)
  • applied geophysics
  • climate change
  • land use/cover change
  • anthropogenic impact
  • landscape planning
  • engineering geology
  • ecological studies
  • 2D imaging, 3D imaging and reconstruction
  • machine learning
  • deep learning
  • autonomous field investigation
  • multispectral data analysis
  • hyperspectral data analysis
  • LIDAR data analysis
  • RADAR data analysis
  • aerial photogrammetry
  • crop production
  • crop detection
  • crop monitoring
  • agricultural machinery control
  • automation and robotics
  • leaf area estimation
  • field boundary and cultivated area determination
  • precision seeding
  • modeling and simulation of water table fluctuation
  • wireless sensor networks
  • drones
  • GPS
  • point clouds acquisition and analysis
  • remote sensing time series data analysis
  • environmental impact

Related Special Issues

Published Papers (33 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 7941 KiB  
Article
GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
by Tin Min Htoo, Helmut Yabar and Takeshi Mizunoya
Appl. Sci. 2022, 12(22), 11822; https://doi.org/10.3390/app122211822 - 21 Nov 2022
Cited by 1 | Viewed by 1780
Abstract
In the study of biomass assessment, geospatial modeling-based analysis becomes crucial for the sustainable management of agriculture. Currently, there is no integrated sustainability assessment of the geographic information system (GIS) cluster or suitability analysis for the feedstock of crop residues. In order to [...] Read more.
In the study of biomass assessment, geospatial modeling-based analysis becomes crucial for the sustainable management of agriculture. Currently, there is no integrated sustainability assessment of the geographic information system (GIS) cluster or suitability analysis for the feedstock of crop residues. In order to fill this research gap and support the strategy of bioenergy formulation with the circular economy concept in agriculture residues in Myanmar, this study aims to assess the energy generation potential and site locations of treatment facilities for crop residue, utilizing the integrated assessment of GIS cluster and suitability modeling. The cluster analysis identifies the rice straw as the highest feedstock of crop residues and township-based high/low clusters. In addition, the electricity generation potential is estimated at 279.14 MW for different clusters of rice straw. Moreover, the suitability analysis in the study uses the conceptual model of variables for constraints and factors with the analytical hierarchy process (AHP) technique to evaluate the weights. The suitability analysis found high suitability areas of 14,603 hectares for treatment facilities within the high/low cluster of feedstock for rice straw. The multicriteria and GIS integrated assessment model adopted in this research can support the decision-makers in developing spatial-based strategic planning for bioenergy promotion which will support sustainable farming practices in Myanmar. Additionally, the proposed model is adaptable in study areas with similar feedstock. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

17 pages, 18387 KiB  
Article
Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim
by Zhibao Wang, Lu Bai, Xiaogang Liu, Yuanlin Chen, Man Zhao and Jinhua Tao
Appl. Sci. 2022, 12(22), 11508; https://doi.org/10.3390/app122211508 - 12 Nov 2022
Cited by 1 | Viewed by 1467
Abstract
With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, [...] Read more.
With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

35 pages, 12993 KiB  
Article
Novel Vision Transformer–Based Bi-LSTM Model for LU/LC Prediction—Javadi Hills, India
by Sam Navin Mohanrajan and Agilandeeswari Loganathan
Appl. Sci. 2022, 12(13), 6387; https://doi.org/10.3390/app12136387 - 23 Jun 2022
Cited by 15 | Viewed by 2548
Abstract
Continuous monitoring and observing of the earth’s environment has become interactive research in the field of remote sensing. Many researchers have provided the Land Use/Land Cover information for the past, present, and future for their study areas around the world. This research work [...] Read more.
Continuous monitoring and observing of the earth’s environment has become interactive research in the field of remote sensing. Many researchers have provided the Land Use/Land Cover information for the past, present, and future for their study areas around the world. This research work builds the Novel Vision Transformer–based Bidirectional long-short term memory model for predicting the Land Use/Land Cover Changes by using the LISS-III and Landsat bands for the forest- and non-forest-covered regions of Javadi Hills, India. The proposed Vision Transformer model achieves a good classification accuracy, with an average of 98.76%. The impact of the Land Surface Temperature map and the Land Use/Land Cover classification map provides good validation results, with an average accuracy of 98.38%, during the process of bidirectional long short-term memory–based prediction analysis. The authors also introduced an application-based explanation of the predicted results through the Google Earth Engine platform of Google Cloud so that the predicted results will be more informative and trustworthy to the urban planners and forest department to take proper actions in the protection of the environment. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

11 pages, 4270 KiB  
Article
Rational Sampling Numbers of Soil pH for Spatial Variation: A Case Study from Yellow River Delta in China
by Yingxin Zhang, Mengqi Duan, Shimei Li, Xiaoguang Zhang, Xiangyun Song and Dejie Cui
Appl. Sci. 2022, 12(13), 6376; https://doi.org/10.3390/app12136376 - 23 Jun 2022
Cited by 1 | Viewed by 1005
Abstract
Spatial variation of soil pH is important for the evaluation of environmental quality. A reasonable number of sampling points has an important meaning for accurate quantitative expression on spatial distribution of soil pH and resource savings. Based on the grid distribution point method, [...] Read more.
Spatial variation of soil pH is important for the evaluation of environmental quality. A reasonable number of sampling points has an important meaning for accurate quantitative expression on spatial distribution of soil pH and resource savings. Based on the grid distribution point method, 908, 797, 700, 594, 499, 398, 299, 200, 149, 100, 75 and 50 sampling points, which were randomly selected from 908 sampling points, constituted 12 sample sets. Semi-variance structure analysis was carried out for different point sets, and ordinary Kriging was used for spatial prediction and accuracy verification, and the influence of different sampling points on spatial variation of soil pH was discussed. The results show that the pH value in Kenli County (China) was generally between 7.8 and 8.1, and the soil was alkaline. Semi-variance models fitted by different point sets could reflect the spatial structure characteristics of soil pH with accuracy. With a decrease in the number of sampling points, the Sill value of sample set increased, and the spatial autocorrelation gradually weakened. Considering the prediction accuracy, spatial distribution and investigation cost, a number of sampling points greater than or equal to 150 could satisfy the spatial variation expression of soil pH at the county level in the Yellow River Delta. This is equivalent to taking at least 107 sampling points per 1000 km2. The results in this study are applicable to areas with similar environmental and soil conditions as the Yellow River Delta, and have reference significance for these areas. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

13 pages, 1942 KiB  
Article
Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China
by Wenjing Wang, Mengqi Duan, Xiaoguang Zhang, Xiangyun Song, Xinwei Liu and Dejie Cui
Appl. Sci. 2022, 12(12), 6062; https://doi.org/10.3390/app12126062 - 15 Jun 2022
Cited by 2 | Viewed by 1353
Abstract
Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy [...] Read more.
Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy of spatial variation of SOM. Therefore, it is important scientific work to determine a reasonable number of sampling points under the premise of ensuring accuracy. In this study, Kenli County, a typical area of the Yellow River Delta in China, was taken as an example to investigate the effect of different sampling points on spatial-variation expression of SOM. A total of 12 sample subsets (including 900 samples) were randomly sampled at equal intervals from the 900 sample points, using geographic information system (GIS) technology and geostatistical analyses to explore the optimal number of samples. The results showed that the SOM content in the study area had a lower-middle degree of variation. As the number of sample points decreased, the spatial distribution of SOM showed the gradual weakening of detail-characterization ability; and when the number of sample points was too small (<100), there was a wrong expression that was not consistent with the actual situation. The value of RMSE has no obvious regularity with the change of sample number. The values of both ME and ASE showed a significant inflection point when the number of samples was 150 and remained around 0 and 4 as the number of samples increased, respectively. Combined with the three indicators of ME, RMSE and ASE, collecting at least 150 samples can satisfy the spatial-variation expression of SOM, equivalent to 107 sample points within the area of 1000 km2. The research results could provide important references for investigation of SOM content in areas with similar natural geographical conditions. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

22 pages, 4208 KiB  
Article
A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
by Farzad Kiani, Giovanni Randazzo, Ilkay Yelmen, Amir Seyyedabbasi, Sajjad Nematzadeh, Fateme Aysin Anka, Fahri Erenel, Metin Zontul, Stefania Lanza and Anselme Muzirafuti
Appl. Sci. 2022, 12(12), 6021; https://doi.org/10.3390/app12126021 - 14 Jun 2022
Cited by 10 | Viewed by 2433
Abstract
Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based [...] Read more.
Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

16 pages, 3237 KiB  
Article
Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery
by Manar Ahmed Hamza, Fadwa Alrowais, Jaber S. Alzahrani, Hany Mahgoub, Nermin M. Salem and Radwa Marzouk
Appl. Sci. 2022, 12(11), 5650; https://doi.org/10.3390/app12115650 - 02 Jun 2022
Cited by 6 | Viewed by 1619
Abstract
With recent advances in remote sensing image acquisition and the increasing availability of fine spectral and spatial information, hyperspectral remote sensing images (HSI) have received considerable attention in several application areas such as agriculture, environment, forestry, and mineral mapping, etc. HSIs have become [...] Read more.
With recent advances in remote sensing image acquisition and the increasing availability of fine spectral and spatial information, hyperspectral remote sensing images (HSI) have received considerable attention in several application areas such as agriculture, environment, forestry, and mineral mapping, etc. HSIs have become an essential method for distinguishing crop classes and accomplishing growth information monitoring for precision agriculture, depending upon the fine spectral response to the crop attributes. The recent advances in computer vision (CV) and deep learning (DL) models allow for the effective identification and classification of different crop types on HSIs. This article introduces a novel squirrel search optimization with a deep transfer learning-enabled crop classification (SSODTL-CC) model on HSIs. The proposed SSODTL-CC model intends to identify the crop type in HSIs properly. To accomplish this, the proposed SSODTL-CC model initially derives a MobileNet with an Adam optimizer for the feature extraction process. In addition, an SSO algorithm with a bidirectional long-short term memory (BiLSTM) model is employed for crop type classification. To demonstrate the better performance of the SSODTL-CC model, a wide-ranging experimental analysis is performed on two benchmark datasets, namely dataset-1 (WHU-Hi-LongKou) and dataset-2 (WHU-Hi-HanChuan). The comparative analysis pointed out the better outcomes of the SSODTL-CC model over other models with a maximum of 99.23% and 97.15% on test datasets 1 and 2, respectively. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

29 pages, 8319 KiB  
Article
Using Simulated Pest Models and Biological Clustering Validation to Improve Zoning Methods in Site-Specific Pest Management
by Luis Josué Méndez-Vázquez, Rodrigo Lasa-Covarrubias, Sergio Cerdeira-Estrada and Andrés Lira-Noriega
Appl. Sci. 2022, 12(4), 1900; https://doi.org/10.3390/app12041900 - 11 Feb 2022
Cited by 2 | Viewed by 1712
Abstract
Site-specific pest management (SSPM) is a component of precision agriculture that relies on spatially enabled agronomic data to facilitate pest control practices within management zones rather than whole fields. Recent integration of high-resolution environmental data, multivariate clustering algorithms, and species distribution modeling has [...] Read more.
Site-specific pest management (SSPM) is a component of precision agriculture that relies on spatially enabled agronomic data to facilitate pest control practices within management zones rather than whole fields. Recent integration of high-resolution environmental data, multivariate clustering algorithms, and species distribution modeling has facilitated the development of a novel approach to SSPM that bases zone delineation on environmentally independent subfield units with individual potential to host pest populations (eSSPM). Although the potential benefits of eSSPM are clear, methods currently described for its implementation still demand further evaluation. To offer clear insight into this matter, we used field-level environmental data from a Tahiti lime orchard and realistic simulations of six citrus pests to: (1) generate a series of virtual (i.e., controlled) infestation scenarios suitable for methodological testing purposes, (2) evaluate the utility of nested (i.e., within-cluster) partitioning essays to improve the accuracy of current eSSPM methods, and (3) implement two biological clustering validators to evaluate the performance of 10 clustering algorithms and choose appropriate numbers of management zones during field partitioning essays. Our results demonstrate that: (1) nested partitioning essays outperform zoning methods previously described in eSSPM, (2) more than one clustering algorithm tend to be necessary to generate field partition models that optimize site-specific pest control practices within crop fields, and (3) biological clustering validation is an essential addition to eSSPM zoning methods. Finally, the generated evidence was integrated into an improved workflow for within-field zone delineation with pest control purposes. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

23 pages, 7763 KiB  
Article
Application of Remote Sensing Tools to Assess the Land Use and Land Cover Change in Coatzacoalcos, Veracruz, Mexico
by Josept David Revuelta-Acosta, Edna Suhail Guerrero-Luis, Jose Eduardo Terrazas-Rodriguez, Cristian Gomez-Rodriguez and Gerardo Alcalá Perea
Appl. Sci. 2022, 12(4), 1882; https://doi.org/10.3390/app12041882 - 11 Feb 2022
Cited by 8 | Viewed by 2567
Abstract
Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative [...] Read more.
Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative technology and tools that support their various decision making systems. This study provides the most recent LULC change analysis for the last six years (2015–2021) of Coatzacoalcos, Veracruz, Mexico, one of the most important petrochemical cities in the world and host of the ongoing Interoceanic Corridor project. The analysis was carried out using Landsat 8 Operational Land Imager (OLI) satellite images, ancillary data and ground-based surveys and the Normalized Difference Vegetation Index (NDVI) to identify and to ameliorate the discrimination between four main macro-classes and fourteen classes. The LULC classification was performed using the maximum likelihood classifier (MLC) to produce maps for each year, as it was found to be the best approach when compared to minimum distance (MDM) and spectral angle mapping (SAM) methods. The macro-classes were water, built-up, vegetation and bare soil, whereas the classes were an improved classification within those. Our study achieved both user accuracy (UA) and producer accuracy (PA) above 90% for the proposed macro-classes and classes. The average Kappa coefficient for macro-classes was 0.93, while for classes it was 0.96, both comparable to previous studies. The results from the LULC analysis show that residential, industry and commercial areas slowed down their growth throughout the study period. These changes were associated with socio-economical drivers such as insecurity and lack of economic investments. Groves and trees presented steady behaviors, with small increments during the five-year period. Swamps, on the other hand, significantly degraded, being about 2% of the study area in 2015 and 0.93% in 2021. Dunes and medium and high vegetation densities (∼80%) transitioned mostly to low vegetation densities. This behavior is associated with rainfall below the annual reference and increments of surface runoff due to the loss of vegetation cover. Lastly, the present study seeks to highlight the importance of remote sensing for a better understanding of the dynamics between human–nature interactions and to provide information to assist planners and decision-makers for more sustainable land development. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 5054 KiB  
Article
Three-Dimensional Convolutional Neural Network on Multi-Temporal Synthetic Aperture Radar Images for Urban Flood Potential Mapping in Jakarta
by Indra Riyanto, Mia Rizkinia, Rahmat Arief and Dodi Sudiana
Appl. Sci. 2022, 12(3), 1679; https://doi.org/10.3390/app12031679 - 06 Feb 2022
Cited by 7 | Viewed by 2327
Abstract
Flooding in urban areas is counted as a significant disaster that must be correctly mitigated due to the huge amount of affected people, material losses, hampered economic activity, and flood-related diseases. One of the technologies available for disaster mitigation and prevention is satellites [...] Read more.
Flooding in urban areas is counted as a significant disaster that must be correctly mitigated due to the huge amount of affected people, material losses, hampered economic activity, and flood-related diseases. One of the technologies available for disaster mitigation and prevention is satellites providing image data on previously flooded areas. In most cases, floods occur in conjunction with heavy rain. Thus, from a satellite’s optical sensor, the flood area is mostly covered with clouds which indicates ineffective observation. One solution to this problem is to use Synthetic Aperture Radar (SAR) sensors by observing backscatter differences before and after flood events. This research proposes mapping the flood-prone areas using machine learning to classify the areas using the 3D CNN method. The method was applied on a combination of co-polarized and cross-polarized SAR multi-temporal image datasets covering Jakarta City and the coastal area of Bekasi Regency. Testing with multiple combinations of training/testing data proportion split and a different number of epochs gave the optimum performance at an 80/20 split with 150 epochs achieving an overall accuracy of 0.71 after training in 283 min. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

20 pages, 3680 KiB  
Article
Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
by Loganathan Agilandeeswari, Manoharan Prabukumar, Vaddi Radhesyam, Kumar L. N. Boggavarapu Phaneendra and Alenizi Farhan
Appl. Sci. 2022, 12(3), 1670; https://doi.org/10.3390/app12031670 - 05 Feb 2022
Cited by 29 | Viewed by 4561
Abstract
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers [...] Read more.
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 22540 KiB  
Article
TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
by Muhammad Shahab Alam, Mansoor Alam, Muhammad Tufail, Muhammad Umer Khan, Ahmet Güneş, Bashir Salah, Fazal E. Nasir, Waqas Saleem and Muhammad Tahir Khan
Appl. Sci. 2022, 12(3), 1308; https://doi.org/10.3390/app12031308 - 26 Jan 2022
Cited by 15 | Viewed by 4558
Abstract
Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks [...] Read more.
Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

22 pages, 7166 KiB  
Article
Simulation-Aided Development of a CNN-Based Vision Module for Plant Detection: Effect of Travel Velocity, Inferencing Speed, and Camera Configurations
by Paolo Rommel Sanchez and Hong Zhang
Appl. Sci. 2022, 12(3), 1260; https://doi.org/10.3390/app12031260 - 25 Jan 2022
Cited by 3 | Viewed by 2376
Abstract
In recent years, Convolutional Neural Network (CNN) has become an attractive method to recognize and localize plant species in unstructured agricultural environments. However, developed systems suffer from unoptimized combinations of the CNN model, computer hardware, camera configuration, and travel velocity to prevent missed [...] Read more.
In recent years, Convolutional Neural Network (CNN) has become an attractive method to recognize and localize plant species in unstructured agricultural environments. However, developed systems suffer from unoptimized combinations of the CNN model, computer hardware, camera configuration, and travel velocity to prevent missed detections. Missed detection occurs if the camera does not capture a plant due to slow inferencing speed or fast travel velocity. Furthermore, modularity was less focused on Machine Vision System (MVS) development. However, having a modular MVS can reduce the effort in development as it will allow scalability and reusability. This study proposes the derived parameter, called overlapping rate (ro), or the ratio of the camera field of view (S) and inferencing speed (fps) to the travel velocity (v⇀) to theoretically predict the plant detection rate (rd) of an MVS and aid in developing a CNN-based vision module. Using performance from existing MVS, the values of ro at different combinations of inferencing speeds (2.4 to 22 fps) and travel velocity (0.1 to 2.5 m/s) at 0.5 m field of view were calculated. The results showed that missed detections occurred when ro was less than 1. Comparing the theoretical detection rate (rd,th) to the simulated detection rate (rd,sim) showed that rd,th had a 20% margin of error in predicting plant detection rate at very low travel distances (<1 m), but there was no margin of error when travel distance was sufficient to complete a detection pattern cycle (≥10 m). The simulation results also showed that increasing S or having multiple vision modules reduced missed detection by increasing the allowable v⇀max. This number of needed vision modules was equal to rounding up the inverse of ro. Finally, a vision module that utilized SSD MobileNetV1 with an average effective inferencing speed of 16 fps was simulated, developed, and tested. Results showed that the rd,th and rd,sim had no margin of error in predicting ractual of the vision module at the tested travel velocities (0.1 to 0.3 m/s). Thus, the results of this study showed that ro can be used to predict rd and optimize the design of a CNN-based vision-equipped robot for plant detections in agricultural field operations with no margin of error at sufficient travel distance. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

23 pages, 4520 KiB  
Article
Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications
by Farzad Kiani, Amir Seyyedabbasi, Sajjad Nematzadeh, Fuat Candan, Taner Çevik, Fateme Aysin Anka, Giovanni Randazzo, Stefania Lanza and Anselme Muzirafuti
Appl. Sci. 2022, 12(3), 943; https://doi.org/10.3390/app12030943 - 18 Jan 2022
Cited by 39 | Viewed by 3688
Abstract
The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, [...] Read more.
The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

21 pages, 9924 KiB  
Article
IoT-Ready Temperature Probe for Smart Monitoring of Forest Roads
by Gabriel Gaspar, Juraj Dudak, Maria Behulova, Maximilian Stremy, Roman Budjac, Stefan Sedivy and Boris Tomas
Appl. Sci. 2022, 12(2), 743; https://doi.org/10.3390/app12020743 - 12 Jan 2022
Cited by 11 | Viewed by 2120
Abstract
Currently, we are experiencing an ever-increasing demand for high-quality transportation in the distinctive natural environment of forest roads, which can be characterized by significant weather changes. The need for more effective management of the forest roads environment, a more direct, rapid response to [...] Read more.
Currently, we are experiencing an ever-increasing demand for high-quality transportation in the distinctive natural environment of forest roads, which can be characterized by significant weather changes. The need for more effective management of the forest roads environment, a more direct, rapid response to fire interventions and, finally, the endeavor to expand recreational use of the woods in the growth of tourism are among the key factors. A thorough collection of diagnostic activities conducted on a regular basis, as well as a dataset of long-term monitored attributes of chosen sections, are the foundations of successful road infrastructure management. Our main contribution to this problem is the design of a probe for measuring the temperature profile for utilization in stand-alone systems or as a part of an IoT solution. We have addressed the design of the mechanical and electrical parts with emphasis on the accuracy of the sensor layout in the probe. Based on this design, we developed a simulation model, and compared the simulation results with the experimental results. An experimental installation was carried out which, based on measurements to date, confirmed the proposed probe meets the requirements of practice and will be deployed in a forest road environment. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 2173 KiB  
Article
Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover
by Markku Luotamo, Maria Yli-Heikkilä and Arto Klami
Appl. Sci. 2022, 12(2), 679; https://doi.org/10.3390/app12020679 - 11 Jan 2022
Cited by 6 | Viewed by 1807
Abstract
We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as [...] Read more.
We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 3196 KiB  
Article
Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
by Muhammad Attique Khan, Abdullah Alqahtani, Aimal Khan, Shtwai Alsubai, Adel Binbusayyis, M Munawwar Iqbal Ch, Hwan-Seung Yong and Jaehyuk Cha
Appl. Sci. 2022, 12(2), 593; https://doi.org/10.3390/app12020593 - 07 Jan 2022
Cited by 35 | Viewed by 3826
Abstract
Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is [...] Read more.
Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 36380 KiB  
Article
Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
by Almetwally M. Mostafa, Swarn Avinash Kumar, Talha Meraj, Hafiz Tayyab Rauf, Abeer Ali Alnuaim and Maram Abdullah Alkhayyal
Appl. Sci. 2022, 12(1), 239; https://doi.org/10.3390/app12010239 - 27 Dec 2021
Cited by 37 | Viewed by 6315
Abstract
Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in [...] Read more.
Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in Asian countries such as Pakistan, which is fourth in its production. Several pathological and fungal diseases attack guava plants. Furthermore, postharvest infections might result in significant output losses. A professional opinion is essential for disease analysis due to minor variances in various guava disease symptoms. Farmers’ poor usage of pesticides may result in financial losses due to incorrect diagnosis. Computer-vision-based monitoring is required with developing field guava plants. This research uses a deep convolutional neural network (DCNN)-based data enhancement using color-histogram equalization and the unsharp masking technique to identify different guava plant species. Nine angles from 360∘ were applied to increase the number of transformed plant images. These augmented data were then fed as input into state-of-the-art classification networks. The proposed method was first normalized and preprocessed. A locally collected guava disease dataset from Pakistan was used for the experimental evaluation. The proposed study uses five neural network structures, AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101, to identify different guava plant species. The experimental results proved that ResNet-101 obtained the highest classification results, with 97.74% accuracy. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

16 pages, 3938 KiB  
Article
A Transfer Learning Technique for Inland Chlorophyll-a Concentration Estimation Using Sentinel-3 Imagery
by Muhammad Aldila Syariz, Chao-Hung Lin, Dewinta Heriza, Umboro Lasminto, Bangun Muljo Sukojo and Lalu Muhamad Jaelani
Appl. Sci. 2022, 12(1), 203; https://doi.org/10.3390/app12010203 - 25 Dec 2021
Cited by 2 | Viewed by 2971
Abstract
Chlorophyll-a (Chla) concentration, which serves as a phytoplankton substitute in inland waters, is one of the leading indicators for water quality. Generally, water samples are analyzed in professional laboratories, and Chla concentrations are measured regularly for the purpose of water quality monitoring. However, [...] Read more.
Chlorophyll-a (Chla) concentration, which serves as a phytoplankton substitute in inland waters, is one of the leading indicators for water quality. Generally, water samples are analyzed in professional laboratories, and Chla concentrations are measured regularly for the purpose of water quality monitoring. However, limited spatial water sampling and the labor-intensive nature of data collection make global and long-term monitoring difficult. The developments of remote-sensing optical sensors and technologies make the long-term monitoring of Chla concentrations for an entire water body more achievable. Many studies based on machine learning techniques, such as regression and artificial neural network (ANN) methods, have recently been proposed for Chla concentration estimation using optical satellite images. The methods based on machine learning can achieve accurate estimation. However, overfitting problems may arise because the in situ Chla dataset is generally insufficient to train a complicated machine learning model, which makes trained models inapplicable. In this study, an ANN model containing three convolutional and two fully connected layers with 4953 unknown parameters is designed. A transfer learning method, consisting of model pretraining, main-training, and fine-tuning stages, is proposed to ease the problem of insufficient in situ samples. In the model pretraining stage, the ANN model is pretrained and initialized using samples derived from an existing Chla concentration model. The pretrained ANN model is then fine-tuned using the proposed transfer learning technique with in situ samples collected in five different campaigns carried out during early 2019 from Laguna Lake, the Philippines. Before the transfer learning, data augmentation and rebalancing methods are conducted to enrich the variability and to near-uniformly distribute the in situ samples in Chla concentration space, respectively. To estimate the alleviation of model overfitting, the trained ANN model, using an in situ dataset from Laguna Lake, was tested using an in situ dataset from Lake Victoria, Uganda, obtained in 2019, which has a similar trophic state as Laguna Lake. The experimental results from Sentinel-3 imagery indicated that the overfitting problem was significantly alleviated and the trained ANN model outperformed related models in terms of the root-mean-squared error of the estimated Chla concentrations. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

12 pages, 6334 KiB  
Article
Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique
by Rabia Saleem, Jamal Hussain Shah, Muhammad Sharif, Mussarat Yasmin, Hwan-Seung Yong and Jaehyuk Cha
Appl. Sci. 2021, 11(24), 11901; https://doi.org/10.3390/app112411901 - 14 Dec 2021
Cited by 29 | Viewed by 5529
Abstract
Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized [...] Read more.
Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

15 pages, 3709 KiB  
Article
One-Dimensional Convolutional Neural Networks for Hyperspectral Analysis of Nitrogen in Plant Leaves
by Razieh Pourdarbani, Sajad Sabzi, Mohammad H. Rohban, José Luis Hernández-Hernández, Iván Gallardo-Bernal, Israel Herrera-Miranda and Ginés García-Mateos
Appl. Sci. 2021, 11(24), 11853; https://doi.org/10.3390/app112411853 - 13 Dec 2021
Cited by 10 | Viewed by 2389
Abstract
Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. [...] Read more.
Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose), and N90% (90% overdose). Hyperspectral data of the samples in the 400–1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N30%, N60% and N90% were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

16 pages, 6176 KiB  
Article
Assessment of Agricultural Water Requirements for Semi-Arid Areas: A Case Study of the Boufakrane River Watershed (Morocco)
by Mohammed El Hafyani, Ali Essahlaoui, Kimberley Fung-Loy, Jason A. Hubbart and Anton Van Rompaey
Appl. Sci. 2021, 11(21), 10379; https://doi.org/10.3390/app112110379 - 05 Nov 2021
Cited by 7 | Viewed by 2492
Abstract
This work was undertaken to develop a low-cost but reliable assessment method for agricultural water requirements in semi-arid locations based on remote sensing data/techniques. In semi-arid locations, water resources are often limited, and long-term water consumption may exceed the natural replenishment rates of [...] Read more.
This work was undertaken to develop a low-cost but reliable assessment method for agricultural water requirements in semi-arid locations based on remote sensing data/techniques. In semi-arid locations, water resources are often limited, and long-term water consumption may exceed the natural replenishment rates of groundwater reservoirs. Sustainable land management in these locations must include tools that facilitate assessment of the impact of potential future land use changes. Agricultural practices in the Boufakrane River watershed (Morocco) were used as a case study application. Land use practices were mapped at the thematic resolution of individual crops, using a total of 13 images generated from the Sentinel-2 satellites. Using a supervised classification scheme, crop types were identified as cereals, other crops followed by cereals, vegetables, olive trees, and fruit trees. Two classifiers were used, namely Support vector machine (SVM) and Random forest (RF). A validation of the classified parcels showed a high overall accuracy of 89.76% for SVM and 84.03% for RF. Results showed that cereal is the most represented species, covering 8870.43 ha and representing 52.42% of the total area, followed by olive trees with 4323.18 ha and a coverage rate of 25%. Vegetables and other crops followed by cereals cover 1530.06 ha and 1661.45 ha, respectively, representing 9.4% and 9.8% of the total area. In the last rank, fruit trees occupy only 3.67% of the total area, with 621.06 ha. The Food and Agriculture Organization (FAO) free software was used to overlay satellite data images with those of climate for agricultural water resources management in the region. This process facilitated estimations of irrigation water requirements for all crop types, taking into account total potential evapotranspiration, effective rainfall, and irrigation water requirements. Results showed that olive trees, fruit trees, and other crops followed by cereals are the most water demanding, with irrigation requirements exceeding 500 mm. The irrigation requirements of cereals and vegetables are lower than those of other classes, with amounts of 300 mm and 150 mm, respectively. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

28 pages, 1648 KiB  
Article
Optimal Versus Equal Dimensions of Round Bales of Agricultural Materials Wrapped with Plastic Film—Conflict or Compliance?
by Anna Stankiewicz
Appl. Sci. 2021, 11(21), 10246; https://doi.org/10.3390/app112110246 - 01 Nov 2021
Viewed by 1294
Abstract
For the assumed bale volume, its dimensions (diameter, height), minimizing the consumption of the plastic film used for bale wrapping with the combined 3D method, depend on film and wrapping parameters. Incorrect selection of these parameters may result in an optimal bale diameter, [...] Read more.
For the assumed bale volume, its dimensions (diameter, height), minimizing the consumption of the plastic film used for bale wrapping with the combined 3D method, depend on film and wrapping parameters. Incorrect selection of these parameters may result in an optimal bale diameter, which differs significantly from its height, while in agricultural practice bales with diameters equal or almost equal to the height dominate. The aim of the study is to formulate and solve the problem of selecting such dimensions of the bale with a given volume that the film consumption is minimal and, simultaneously, the bale diameter is equal or almost equal to its height. Necessary and sufficient conditions for such equilibria of the optimal bale dimensions are derived in the form of algebraic equations and inequalities. Four problems of the optimal bale dimension design guaranteeing assumed equilibrium of diameter and height are formulated and solved; both free and fixed bale volume are considered. Solutions of these problems are reduced to solving the sets of simple algebraic equations and inequalities with respect to two variables: integer number of film layers and continuous overlap ratio in bottom layers. Algorithms were formulated and examples regarding large bales demonstrate that they can handle the optimal dimensions’ equilibria problems. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

14 pages, 3212 KiB  
Article
Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
by Shilan Felegari, Alireza Sharifi, Kamran Moravej, Muhammad Amin, Ahmad Golchin, Anselme Muzirafuti, Aqil Tariq and Na Zhao
Appl. Sci. 2021, 11(21), 10104; https://doi.org/10.3390/app112110104 - 28 Oct 2021
Cited by 43 | Viewed by 5477
Abstract
Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined [...] Read more.
Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

12 pages, 2516 KiB  
Article
Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes
by Joon-Keat Lai and Wen-Shin Lin
Appl. Sci. 2021, 11(21), 10076; https://doi.org/10.3390/app112110076 - 27 Oct 2021
Cited by 6 | Viewed by 6183
Abstract
The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed [...] Read more.
The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

20 pages, 3065 KiB  
Article
Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries
by Aimin Li, Meng Fan, Guangduo Qin, Youcheng Xu and Hailong Wang
Appl. Sci. 2021, 11(21), 10062; https://doi.org/10.3390/app112110062 - 27 Oct 2021
Cited by 15 | Viewed by 2342
Abstract
Monitoring open water bodies accurately is important for assessing the role of ecosystem services in the context of human survival and climate change. There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water [...] Read more.
Monitoring open water bodies accurately is important for assessing the role of ecosystem services in the context of human survival and climate change. There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water index (NDWI), modified NDWI (MNDWI), and machine learning algorithms. Based on Landsat-8 remote sensing images, this study focuses on the effects of six machine learning algorithms and three threshold methods used to extract water bodies, evaluates the transfer performance of models applied to remote sensing images in different periods, and compares the differences among these models. The results are as follows. (1) Various algorithms require different numbers of samples to reach their optimal consequence. The logistic regression algorithm requires a minimum of 110 samples. As the number of samples increases, the order of the optimal model is support vector machine, neural network, random forest, decision tree, and XGBoost. (2) The accuracy evaluation performance of each machine learning on the test set cannot represent the local area performance. (3) When these models are directly applied to remote sensing images in different periods, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decrease range of 0.33–66.52%, and the differences among the different algorithm performances in the three areas are obvious. Generally, the decision tree algorithm has good transfer performance among the machine learning algorithms with area under curve (AUC) indexes of 0.790, 0.518, and 0.697 in the three areas, respectively, and the average value is 0.668. The Otsu threshold algorithm is the optimal among threshold methods, with AUC indexes of 0.970, 0.617, and 0.908 in the three regions respectively and an average AUC of 0.832. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

14 pages, 15743 KiB  
Article
Modeling and Spatiotemporal Mapping of Water Quality through Remote Sensing Techniques: A Case Study of the Hassan Addakhil Dam
by Anas El Ouali, Mohammed El Hafyani, Allal Roubil, Abderrahim Lahrach, Ali Essahlaoui, Fatima Ezzahra Hamid, Anselme Muzirafuti, Dimitrios S. Paraforos, Stefania Lanza and Giovanni Randazzo
Appl. Sci. 2021, 11(19), 9297; https://doi.org/10.3390/app11199297 - 07 Oct 2021
Cited by 12 | Viewed by 2690
Abstract
With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a [...] Read more.
With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R2, the root mean square error and p-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 µg/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 14692 KiB  
Article
Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration
by Yan Liu, Sha Zhang, Jiahua Zhang, Lili Tang and Yun Bai
Appl. Sci. 2021, 11(18), 8649; https://doi.org/10.3390/app11188649 - 17 Sep 2021
Cited by 10 | Viewed by 2198
Abstract
Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely [...] Read more.
Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely focused on cropland. In this study, we trained two models of estimating croplands ET. The first is Medlyn-Penman-Monteith (Medlyn-PM) model. It uses artificial neural network (ANN)-derived gross primary production along with Medlyn’s stomatal conductance to compute surface conductance (Gs), and the computed Gs is used to estimate ET using the PM equation. The second model, termed ANN-PM, directly uses ANN to construct Gs and simulate ET using the PM equation. The results showed that the two models can reasonably reproduce ET with ANN-PM showing a better performance, as indicated by the lower error and higher determination coefficients. The results also showed that the performances of ANN-PM without the facilitation of any remote sensing (RS) factors degraded significantly compared to the versions that used RS factors. We also evidenced that ANN-PM can reasonably characterize the time-series changes of ET at sites having a dry climate. The ANN-PM method can reasonably estimate the ET of croplands under different environmental conditions. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

21 pages, 19008 KiB  
Article
WebGIS Implementation for Dynamic Mapping and Visualization of Coastal Geospatial Data: A Case Study of BESS Project
by Giovanni Randazzo, Franco Italiano, Anton Micallef, Agostino Tomasello, Federica Paola Cassetti, Anthony Zammit, Sebastiano D’Amico, Oliver Saliba, Maria Cascio, Franco Cavallaro, Antonio Crupi, Marco Fontana, Francesco Gregorio, Stefania Lanza, Emanuele Colica and Anselme Muzirafuti
Appl. Sci. 2021, 11(17), 8233; https://doi.org/10.3390/app11178233 - 05 Sep 2021
Cited by 33 | Viewed by 5919
Abstract
Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored [...] Read more.
Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored with the creation of a WebGIS portal that will allow local and regional stakeholders/authorities obtain an easy remote access tool to monitor the status of pocket beaches (PB) in the Maltese Archipelago and Sicily. In this paper, we provide a methodological approach for the implementation of a WebGIS necessary for very detailed dynamic mapping and visualization of geospatial coastal data; the description of the dataset necessary for the monitoring of coastal areas, especially the PBs; and a demonstration of a case study for the PBs of Sicily and Malta by using the methodology and the dataset used during the BESS project. Detailed steps involved in the creation of the WebGIS are presented. These include data preparation, data storage, and data publication and transformation into geo-services. With the help of different Open Geospatial Consortium protocols, the WebGIS displays different layers of information for 134 PBs including orthophotos, sedimentological/geomorphological beach characteristics, shoreline evolution, geometric and morphological parameters, shallow water bathymetry, and photographs of pocket beaches. The WebGIS allows not only for identifying, evaluating, and directing potential solutions to present and arising issues, but also enables public access and involvement. It reflects a platform for future local and regional coastal zone monitoring and management, by promoting public/private involvement in addressing coastal issues and providing local public administrations with an improved technology to monitor coastal changes and help better plan suitable interventions. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

Review

Jump to: Research

19 pages, 3462 KiB  
Review
IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges
by Vu Khanh Quy, Nguyen Van Hau, Dang Van Anh, Nguyen Minh Quy, Nguyen Tien Ban, Stefania Lanza, Giovanni Randazzo and Anselme Muzirafuti
Appl. Sci. 2022, 12(7), 3396; https://doi.org/10.3390/app12073396 - 27 Mar 2022
Cited by 129 | Viewed by 31993
Abstract
The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural [...] Read more.
The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 1355 KiB  
Review
The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review
by Nursyazyla Sulaiman, Nik Norasma Che’Ya, Muhammad Huzaifah Mohd Roslim, Abdul Shukor Juraimi, Nisfariza Mohd Noor and Wan Fazilah Fazlil Ilahi
Appl. Sci. 2022, 12(5), 2570; https://doi.org/10.3390/app12052570 - 01 Mar 2022
Cited by 15 | Viewed by 5238
Abstract
Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years [...] Read more.
Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

25 pages, 1536 KiB  
Review
Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
by Rhushalshafira Rosle, Nik Norasma Che’Ya, Yuhao Ang, Fariq Rahmat, Aimrun Wayayok, Zulkarami Berahim, Wan Fazilah Fazlil Ilahi, Mohd Razi Ismail and Mohamad Husni Omar
Appl. Sci. 2021, 11(22), 10701; https://doi.org/10.3390/app112210701 - 12 Nov 2021
Cited by 9 | Viewed by 5675
Abstract
This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in [...] Read more.
This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

24 pages, 3991 KiB  
Review
Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review
by Muaadh A. Alsoufi, Shukor Razak, Maheyzah Md Siraj, Ibtehal Nafea, Fuad A. Ghaleb, Faisal Saeed and Maged Nasser
Appl. Sci. 2021, 11(18), 8383; https://doi.org/10.3390/app11188383 - 09 Sep 2021
Cited by 83 | Viewed by 10413
Abstract
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart [...] Read more.
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction. Full article
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-33
Back to TopTop