Sensors

Research

Jump to: Review

1119 KiB

Open AccessArticle

Relationship between Remote Sensing Data, Plant Biomass and Soil Nitrogen Dynamics in Intensively Managed Grasslands under Controlled Conditions

by Christoph Knoblauch, Conor Watson, Clara Berendonk, Rolf Becker, Nicole Wrage-Mönnig and Florian Wichern

Sensors 2017, 17(7), 1483; https://doi.org/10.3390/s17071483 - 23 Jun 2017

Cited by 14 | Viewed by 5625

Abstract

The sustainable use of grasslands in intensive farming systems aims to optimize nitrogen (N) inputs to increase crop yields and decrease harmful losses to the environment at the same time. To achieve this, simple optical sensors may provide a non-destructive, time- and cost-effective [...] Read more.

The sustainable use of grasslands in intensive farming systems aims to optimize nitrogen (N) inputs to increase crop yields and decrease harmful losses to the environment at the same time. To achieve this, simple optical sensors may provide a non-destructive, time- and cost-effective tool for estimating plant biomass in the field, considering spatial and temporal variability. However, the plant growth and related N uptake is affected by the available N in the soil, and therefore, N mineralization and N losses. These soil N dynamics and N losses are affected by the N input and environmental conditions, and cannot easily be determined non-destructively. Therefore, the question arises: whether a relationship can be depicted between N fertilizer levels, plant biomass and N dynamics as indicated by nitrous oxide (N₂O) losses and inorganic N levels. We conducted a standardized greenhouse experiment to explore the potential of spectral measurements for analyzing yield response, N mineralization and N₂O emissions in a permanent grassland. Ryegrass was subjected to four mineral fertilizer input levels over 100 days (four harvests) under controlled environmental conditions. The soil temperature and moisture content were automatically monitored, and the emission rates of N₂O and carbon dioxide (CO₂) were detected frequently. Spectral measurements of the swards were performed directly before harvesting. The normalized difference vegetation index (NDVI) and simple ratio (SR) were moderately correlated with an increasing biomass as affected by fertilization level. Furthermore, we found a non-linear response of increasing N₂O emissions to elevated fertilizer levels. Moreover, inorganic N and extractable organic N levels at the end of the experiment tended to increase with the increasing N fertilizer addition. However, microbial biomass C and CO₂ efflux showed no significant differences among fertilizer treatments, reflecting no substantial changes in the soil biological pool size and the extent of the C mineralization. Neither the NDVI nor SR, nor the plant biomass, were related to cumulative N₂O emissions or inorganic N at harvesting. Our results verify the usefulness of optical sensors for biomass detection, and show the difficulty in linking spectral measurements of plant traits to N processes in the soil, despite that the latter affects the former. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

6569 KiB

Open AccessArticle

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production

by Marston Héracles Domingues Franceschini, Harm Bartholomeus, Dirk Van Apeldoorn, Juha Suomalainen and Lammert Kooistra

Sensors 2017, 17(6), 1428; https://doi.org/10.3390/s17061428 - 18 Jun 2017

Cited by 49 | Viewed by 15250 | Correction

Abstract

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate [...] Read more.

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm⁻²), leaf area index (RMSE = 0.67 m²·m⁻²), canopy chlorophyll (RMSE = 0.24 g·m⁻²) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm⁻², 0.85 m²·m⁻², 0.28 g·m⁻² and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CI_g provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

1389 KiB

Open AccessArticle

Root System Water Consumption Pattern Identification on Time Series Data

by Manuel Figueroa and Christopher Pope

Sensors 2017, 17(6), 1410; https://doi.org/10.3390/s17061410 - 16 Jun 2017

Cited by 13 | Viewed by 4909

Abstract

In agriculture, soil and meteorological sensors are used along low power networks to capture data, which allows for optimal resource usage and minimizing environmental impact. This study uses time series analysis methods for outliers’ detection and pattern recognition on soil moisture sensor data [...] Read more.

In agriculture, soil and meteorological sensors are used along low power networks to capture data, which allows for optimal resource usage and minimizing environmental impact. This study uses time series analysis methods for outliers’ detection and pattern recognition on soil moisture sensor data to identify irrigation and consumption patterns and to improve a soil moisture prediction and irrigation system. This study compares three new algorithms with the current detection technique in the project; the results greatly decrease the number of false positives detected. The best result is obtained by the Series Strings Comparison (SSC) algorithm averaging a precision of 0.872 on the testing sets, vastly improving the current system’s 0.348 precision. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

2373 KiB

Open AccessArticle

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

by Oscar S. Dalmau, Teresa E. Alarcón and Francisco E. Oliva

Sensors 2017, 17(6), 1373; https://doi.org/10.3390/s17061373 - 13 Jun 2017

Cited by 6 | Viewed by 4212

Abstract

Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources [...] Read more.

Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources grows, as in the case of satellite images, the histogram construction becomes more difficult due to the high dimensionality of the feature space. In this work, we propose a generalization of Gaussian Markov Measure Field Models and provide a probabilistic segmentation scheme, which fuses multiple information sources for image segmentation. In particular, we apply the general model to classify types of crops in satellite images. The proposed method allows us to combine several feature spaces. For this purpose, the method requires prior information for building a 3D histogram for each considered feature space. Based on previous histograms, we can compute the likelihood of each site of the image to belong to a class. The computed likelihoods are the main input of the proposed algorithm and are combined in the proposed model using a contrast criteria. Different feature spaces are analyzed, among them are 6 spectral bands from LANDSAT 5 TM, 3 principal components from PCA on 6 spectral bands and 3 principal components from PCA applied on 10 vegetation indices. The proposed algorithm was applied to a real image and obtained excellent results in comparison to different classification algorithms used in crop classification. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

7832 KiB

Open AccessArticle

A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions

by Francesco Nutini, Daniela Stroppiana, Lorenzo Busetto, Dario Bellingeri, Chiara Corbari, Marco Mancini, Enrico Zini, Pietro Alessandro Brivio and Mirco Boschetti

Sensors 2017, 17(6), 1338; https://doi.org/10.3390/s17061338 - 9 Jun 2017

Cited by 4 | Viewed by 4898

Abstract

The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS [...] Read more.

The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1) data to estimate the Evaporative Fraction (EF) at 1 km resolution, on a daily basis. Measurements at two eddy covariance towers located within the study area have been exploited to assess the reliability of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EF_w) were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EF_w showed a temporal behavior consistent with growing cycles and agro-practices of the main crops cultivated in the study area (rice, forages and corn). Comparison with official regional corn yield data showed that variations in EF_w cumulated over summer are related with crop production shortages induced by water scarcity. These results suggest that weekly-averaged EF estimated from MODIS data is sensible to water stress conditions and can be used as an indicator of crops’ moisture conditions at agronomical district level. Advantages and disadvantages of the proposed approach to provide information useful to issue operational near real time bulletins on crop conditions at regional scale are discussed. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

4189 KiB

Open AccessArticle

Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data

by Ping Xu, Junfeng Liu, Lingyun Xue, Jingcheng Zhang and Bo Qiu

Sensors 2017, 17(6), 1322; https://doi.org/10.3390/s17061322 - 7 Jun 2017

Cited by 10 | Viewed by 4342

Abstract

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing [...] Read more.

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing (AGDCS) algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The spectral characteristics of hyperspectral data are analyzed and the joint sparse model is constructed. The spectral bands are adaptively grouped and the hyperspectral data are compressed and reconstructed on the basis of grouping. The experimental results showed that, compared with orthogonal matching pursuit (OMP) and gradient projection for sparse reconstruction (GPSR), AGDCS can significantly improve the visual effect of image reconstruction in the spatial domain. The peak signal-to-noise ratio (PSNR) at a low sampling rate (the sampling rate is lower than 0.2) increases by 13.72 dB than OMP and 1.66 dB than GPSR. In the spectral domain, the average normalized root mean square error, the mean absolute percentage error, and the mean absolute error of AGDCS is 35.38%, 31.83%, and 33.33% lower than GPSR, respectively. Additionally, AGDCS can achieve relatively high reconstructed efficiency. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

12259 KiB

Open AccessArticle

Using Color, Texture and Object-Based Image Analysis of Multi-Temporal Camera Data to Monitor Soil Aggregate Breakdown

by Irena Ymeti, Harald Van der Werff, Dhruba Pikha Shrestha, Victor G. Jetten, Caroline Lievens and Freek Van der Meer

Sensors 2017, 17(6), 1241; https://doi.org/10.3390/s17061241 - 30 May 2017

Cited by 9 | Viewed by 5589

Abstract

Remote sensing has shown its potential to assess soil properties and is a fast and non-destructive method for monitoring soil surface changes. In this paper, we monitor soil aggregate breakdown under natural conditions. From November 2014 to February 2015, images and weather data [...] Read more.

Remote sensing has shown its potential to assess soil properties and is a fast and non-destructive method for monitoring soil surface changes. In this paper, we monitor soil aggregate breakdown under natural conditions. From November 2014 to February 2015, images and weather data were collected on a daily basis from five soils susceptible to detachment (Silty Loam with various organic matter content, Loam and Sandy Loam). Three techniques that vary in image processing complexity and user interaction were tested for the ability of monitoring aggregate breakdown. Considering that the soil surface roughness causes shadow cast, the blue/red band ratio is utilized to observe the soil aggregate changes. Dealing with images with high spatial resolution, image texture entropy, which reflects the process of soil aggregate breakdown, is used. In addition, the Huang thresholding technique, which allows estimation of the image area occupied by soil aggregate, is performed. Our results show that all three techniques indicate soil aggregate breakdown over time. The shadow ratio shows a gradual change over time with no details related to weather conditions. Both the entropy and the Huang thresholding technique show variations of soil aggregate breakdown responding to weather conditions. Using data obtained with a regular camera, we found that freezing–thawing cycles are the cause of soil aggregate breakdown. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

6885 KiB

Open AccessArticle

Sensor Fusion of a Mobile Device to Control and Acquire Videos or Images of Coffee Branches and for Georeferencing Trees

by Paula Jimena Ramos Giraldo, Álvaro Guerrero Aguirre, Carlos Mario Muñoz, Flavio Augusto Prieto and Carlos Eugenio Oliveros

Sensors 2017, 17(4), 786; https://doi.org/10.3390/s17040786 - 6 Apr 2017

Cited by 5 | Viewed by 6230

Abstract

Smartphones show potential for controlling and monitoring variables in agriculture. Their processing capacity, instrumentation, connectivity, low cost, and accessibility allow farmers (among other users in rural areas) to operate them easily with applications adjusted to their specific needs. In this investigation, the integration [...] Read more.

Smartphones show potential for controlling and monitoring variables in agriculture. Their processing capacity, instrumentation, connectivity, low cost, and accessibility allow farmers (among other users in rural areas) to operate them easily with applications adjusted to their specific needs. In this investigation, the integration of inertial sensors, a GPS, and a camera are presented for the monitoring of a coffee crop. An Android-based application was developed with two operating modes: (i) Navigation: for georeferencing trees, which can be as close as 0.5 m from each other; and (ii) Acquisition: control of video acquisition, based on the movement of the mobile device over a branch, and measurement of image quality, using clarity indexes to select the most appropriate frames for application in future processes. The integration of inertial sensors in navigation mode, shows a mean relative error of ±0.15 m, and total error ±5.15 m. In acquisition mode, the system correctly identifies the beginning and end of mobile phone movement in 99% of cases, and image quality is determined by means of a sharpness factor which measures blurriness. With the developed system, it will be possible to obtain georeferenced information about coffee trees, such as their production, nutritional state, and presence of plagues or diseases. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

8222 KiB

Open AccessArticle

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

by Jie Wang, Changwei Shen, Na Liu, Xin Jin, Xueshan Fan, Caixia Dong and Yangchun Xu

Sensors 2017, 17(3), 538; https://doi.org/10.3390/s17030538 - 8 Mar 2017

Cited by 49 | Viewed by 7821

Abstract

Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and [...] Read more.

Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha⁻¹. The mid-portion leaves on the year’s shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets—both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration (n = 1000) and validation (n = 420) of this model resulted in high R² values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R² = 0.77) and 2014 (R² = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24–27 g/kg. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

12558 KiB

Open AccessArticle

VineSens: An Eco-Smart Decision-Support Viticulture System

by Josman P. Pérez-Expósito, Tiago M. Fernández-Caramés, Paula Fraga-Lamas and Luis Castedo

Sensors 2017, 17(3), 465; https://doi.org/10.3390/s17030465 - 25 Feb 2017

Cited by 61 | Viewed by 12380

Abstract

This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that [...] Read more.

This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that allow us to obtain detailed knowledge on different viticulture processes. Thanks to the use of epidemiological models, VineSens is able to propose a custom control plan to prevent diseases like one of the most feared by vine growers: downy mildew. VineSens generates alerts that warn farmers about the measures that have to be taken and stores the historical weather data collected from different spots of the vineyard. Such data can then be accessed through a user-friendly web-based interface that can be accessed through the Internet by using desktop or mobile devices. VineSens was deployed at the beginning in 2016 in a vineyard in the Ribeira Sacra area (Galicia, Spain) and, since then, its hardware and software have been tested to prevent the development of downy mildew, showing during its first season that the system can led to substantial savings, to decrease the amount of phytosanitary products applied, and, as a consequence, to obtain a more ecologically sustainable and healthy wine. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

39184 KiB

Open AccessArticle

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products

by Yang Zheng, Bingfang Wu, Miao Zhang and Hongwei Zeng

Sensors 2016, 16(12), 2099; https://doi.org/10.3390/s16122099 - 10 Dec 2016

Cited by 59 | Viewed by 7607

Abstract

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection [...] Read more.

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5’s spatial resolution and at MODIS’s temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R² of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R² of the SOS ranging from 0.68 to 0.86 and with an R² of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

1265 KiB

Open AccessArticle

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

by Francesca Orlando, Ermes Movedi, Davide Coduto, Simone Parisi, Lucio Brancadoro, Valentina Pagani, Tommaso Guarneri and Roberto Confalonieri

Sensors 2016, 16(12), 2004; https://doi.org/10.3390/s16122004 - 26 Nov 2016

Cited by 31 | Viewed by 9296

Abstract

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six [...] Read more.

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 (R² = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included (R² = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of R², even in presence of the outlying value (R² = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

3344 KiB

Open AccessArticle

A Data Transfer Fusion Method for Discriminating Similar Spectral Classes

by Qingyan Wang and Junping Zhang

Sensors 2016, 16(11), 1895; https://doi.org/10.3390/s16111895 - 14 Nov 2016

Cited by 3 | Viewed by 3906

Abstract

Hyperspectral data provide new capabilities for discriminating spectrally similar classes, but such class signatures sometimes will be difficult to analyze. To incorporate reliable useful information could help, but at the same time, may also lead increased dimensionality of the feature vector making the [...] Read more.

Hyperspectral data provide new capabilities for discriminating spectrally similar classes, but such class signatures sometimes will be difficult to analyze. To incorporate reliable useful information could help, but at the same time, may also lead increased dimensionality of the feature vector making the hyperspectral data larger than expected. It is challenging to apply discriminative information from these training data to testing data that are not in the same feature space and with different data distributions. A data fusion method based on transfer learning is proposed, in which transfer learning is introduced into boosting algorithm, and other out-date data are used to instruct hyperspectral image classification. In order to validate the method, experiments are conducted on EO-1 Hyperion hyperspectral data and ROSIS hyperspectral data. Significant improvements have been achieved in terms of accuracy compared to the results generated by conventional classification approaches. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

Review

Jump to: Research

1137 KiB

Open AccessReview

Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review

by Haider Mahmood Jawad, Rosdiadee Nordin, Sadik Kamel Gharghan, Aqeel Mahmood Jawad and Mahamod Ismail

Sensors 2017, 17(8), 1781; https://doi.org/10.3390/s17081781 - 3 Aug 2017

Cited by 496 | Viewed by 39341

Abstract

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead [...] Read more.

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data. Full article

(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Precision Agriculture and Remote Sensing Data Fusion

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (14 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI