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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (1 September 2021) | Viewed by 67361

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Special Issue Editors

Dr. François Waldner

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Guest Editor

CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, QLD 4067, Australia
Interests: agriculture; machine learning; time series analysis; yield estimation; classification

Dr. Raphael d'Andrimont

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Guest Editor

Joint Research Centre (JRC), European Commission, 1050 Brussels, Belgium
Interests: food systems; agriculture; remote sensing; in-situ data

Dr. Andre Colaço

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Guest Editor

Agriculture & Food, CSIRO, Waite, SA 5064, Australia
Interests: precision agriculture; digital agriculture; farming systems

Special Issue Information

Dear Colleagues,

On-farm, digital agriculture promises to help farmers to be more efficient through data-driven decisions while minimising the impact of farming on the environment. For instance, digital data can help to develop and guide site-specific management strategies in precision agriculture so that systems are optimised at the site-specific (within-field) scale. Off-farm, digital agriculture has many benefits across the supply chain (industry, agribusiness and government). For instance, digital technologies have the potential to even up information asymmetry, which increases trust and reduces costs among different players and to monitor food security globally by providing timely and accurate crop production estimates. Since the early days of digital agriculture, remote sensing has been a major source of data and has been integrated into diagnostic analytics (e.g., crop type mapping), real-time analytics (e.g., biophysical variables, evapotranspiration), predictive analytics (e.g., yield forecasting) and prescriptive analytics (e.g., optimisation of fertiliser application) necessary to monitor crop growth and crop production from the field scale to the global scale. Given the recent advances in machine learning and artificial intelligence and the unprecedented availability of satellite imagery and other sensors, the remote-sensing community has never been better positioned to help to deliver the promises of digital agriculture. New challenges have also emerged, such as sufficient access to in situ data used for model training or timely data processing. More and more heterogeneous data (field sensors communicating through the Internet of Things) are being integrated with remotely-sensed data into workflows delivering timely and actionable information to end-users. This Special Issue of Remote Sensing invites papers that apply remotely-sensed data from ground, aerial or satellite platforms and that develop innovative solutions to circumvent constraints to their operationalisation. This includes topics such as:

Cropland, fallow land and crop type mapping;
Yield prediction and yield forecasting;
Rangeland and pasture-land monitoring;
Synergies between Earth Observation, in situ data, in-field sensors, street-level imagery and meteorological data;
Efficient sampling approaches for in situ data collection;
Phenology mapping;
Integration of remote sensing and crop models;
Essential agricultural variables;
Field boundary extraction;
Field-level recommendations;
Remote identification of management practices;
Upscaling of ground-based and drone-based observations;
Crop health and nutrition mapping;
Site-specific crop management;
Sensor-based variable rate application;
Digital crop phenotyping;
Monitoring of soil parameters and land-degradation processes;
Commercial satellite constellations (CubeSats++).

Dr. François Waldner
Dr. Raphael d'Andrimont
Dr. Andre Colaço
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Digital agriculture
Precision agriculture
Remote sensing
Optical images
Radar images
Thermal images
Hyperspectral images
LiDAR
Data fusion
Time series
In situ data
Machine learning
Artificial intelligence

Published Papers (13 papers)

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15 pages, 7478 KiB

Open AccessArticle

Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology

by André Freitas Colaço, Michael Schaefer and Robert G. V. Bramley

Remote Sens. 2021, 13(16), 3218; https://doi.org/10.3390/rs13163218 - 13 Aug 2021

Cited by 5 | Viewed by 3063

Abstract

Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha⁻¹. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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20 pages, 5222 KiB

Open AccessArticle

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

by Juan Fernández-Novales, Verónica Saiz-Rubio, Ignacio Barrio, Francisco Rovira-Más, Andrés Cuenca-Cuenca, Fernando Santos Alves, Joana Valente, Javier Tardaguila and María Paz Diago

Remote Sens. 2021, 13(14), 2830; https://doi.org/10.3390/rs13142830 - 19 Jul 2021

Cited by 22 | Viewed by 3580

Abstract

There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, very few approaches with ground robots have served as moving platforms for carrying non-invasive sensors to deliver field maps that help growers in decision making. The goal of this work is to demonstrate the capability of the VineScout (developed in the context of a H2020 EU project), a ground robot designed to assess and map vineyard water status using thermal infrared radiometry in commercial vineyards. The trials were carried out in Douro Superior (Portugal) under different irrigation treatments during seasons 2019 and 2020. Grapevines of Vitis vinifera L. Touriga Nacional were monitored at different timings of the day using leaf water potential (Ψ_l) as reference indicators of plant water status. Grapevines’ canopy temperature (T_c) values, recorded with an infrared radiometer, as well as data acquired with an environmental sensor (T_air, RH, and AP) and NDVI measurements collected with a multispectral sensor were automatically saved in the computer of the autonomous robot to assess and map the spatial variability of a commercial vineyard water status. Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r²_cv) of 0.57 in the morning time and a r²_cv of 0.42 in the midday. The root mean square error of cross-validation (RMSE_cv) was 0.191 MPa and 0.139 MPa at morning and midday, respectively. Spatial–temporal variation maps were developed at two different times of the day to illustrate the capability to monitor the grapevine water status in order to reduce the consumption of water, implementing appropriate irrigation strategies and increase the efficiency in the real time vineyard management. The promising outcomes gathered with the VineScout using different sensors based on thermography, multispectral imaging and environmental data disclose the need for further studies considering new variables related with the plant water status, and more grapevine cultivars, seasons and locations to improve the accuracy, robustness and reliability of the predictive models, in the context of precision and sustainable viticulture. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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18 pages, 4020 KiB

Open AccessArticle

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

by Jianxiu Shen and Fiona H. Evans

Remote Sens. 2021, 13(11), 2202; https://doi.org/10.3390/rs13112202 - 4 Jun 2021

Cited by 6 | Viewed by 3236

Abstract

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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24 pages, 66421 KiB

Open AccessArticle

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

by Iliyana D. Dobreva, Henry A. Ruiz-Guzman, Ilse Barrios-Perez, Tyler Adams, Brody L. Teare, Paxton Payton, Mark E. Everett, Mark D. Burow and Dirk B. Hays

Remote Sens. 2021, 13(10), 1896; https://doi.org/10.3390/rs13101896 - 12 May 2021

Cited by 10 | Viewed by 3732

Abstract

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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14 pages, 3874 KiB

Open AccessArticle

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

by Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner and José Paulo Molin

Remote Sens. 2021, 13(2), 232; https://doi.org/10.3390/rs13020232 - 12 Jan 2021

Cited by 30 | Viewed by 7320

Abstract

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha⁻¹ with an R² of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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17 pages, 16611 KiB

Open AccessArticle

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

by Amr Abd-Elrahman, Zhen Guan, Cheryl Dalid, Vance Whitaker, Katherine Britt, Benjamin Wilkinson and Ali Gonzalez

Remote Sens. 2020, 12(21), 3632; https://doi.org/10.3390/rs12213632 - 5 Nov 2020

Cited by 9 | Viewed by 3322

Abstract

Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R² values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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24 pages, 12601 KiB

Open AccessArticle

Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies

by Aiym Orynbaikyzy, Ursula Gessner, Benjamin Mack and Christopher Conrad

Remote Sens. 2020, 12(17), 2779; https://doi.org/10.3390/rs12172779 - 27 Aug 2020

Cited by 72 | Viewed by 7710

Abstract

Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops’ temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features’ relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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23 pages, 3357 KiB

Open AccessArticle

First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices

by Víctor Cicuéndez, Manuel Rodríguez-Rastrero, Laura Recuero, Margarita Huesca, Thomas Schmid, Rosa Inclán, Javier Litago, Víctor Sánchez-Girón and Alicia Palacios-Orueta

Remote Sens. 2020, 12(17), 2724; https://doi.org/10.3390/rs12172724 - 23 Aug 2020

Cited by 1 | Viewed by 2975

Abstract

Rainfed agriculture occupies the majority of the world’s agricultural surface and is expected to increase in the near future causing serious effects on carbon cycle dynamics in the context of climate change. Carbon cycle across several temporal and spatial scales could be studied through spectral indices because they are related to vegetation structure and functioning and hence with carbon fluxes, among them soil respiration (Rs). The aim of this work was to assess Rs linked to crop phenology of a rainfed barley crop throughout two seasons based on spectral indices calculated from field spectroscopy data. The relationships between Rs, Leaf Area Index (LAI) and spectral indices were assessed by linear regression models with the adjusted coefficient of determination (R_adj²). Results showed that most of the spectral indices provided better information than LAI throughout the studied period and that soil moisture and temperature were relevant variables in specific periods. During vegetative stages, indices based on the visible (VIS) region showed the best relationship with Rs. On the other hand, during reproductive stages indices containing the near infrared-shortwave infrared (NIR-SWIR) spectral region and those related to water content showed the highest relationship. The inter-annual variability found in Mediterranean regions was also observed in the estimated ratio of carbon emission to carbon fixation between years. Our results show the potential capability of spectral information to assess soil respiration linked to crop phenology across several temporal and spatial scales. These results can be used as a basis for the utilization of other remote information derived from satellites or airborne sensors to monitor crop carbon balances. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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21 pages, 4684 KiB

Open AccessArticle

Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data

by Xiaobin Xu, Cong Teng, Yu Zhao, Ying Du, Chunqi Zhao, Guijun Yang, Xiuliang Jin, Xiaoyu Song, Xiaohe Gu, Raffaele Casa, Liping Chen and Zhenhai Li

Remote Sens. 2020, 12(8), 1349; https://doi.org/10.3390/rs12081349 - 24 Apr 2020

Cited by 24 | Viewed by 3566

Abstract

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R² = 1.00) and RE (R² = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R² = 0.524) demonstrated higher accuracy than the empirical linear model (R² = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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19 pages, 9625 KiB

Open AccessArticle

Combining Fractional Cover Images with One-Class Classifiers Enables Near Real-Time Monitoring of Fallows in the Northern Grains Region of Australia

by Liya Zhao, François Waldner, Peter Scarth, Benjamin Mack and Zvi Hochman

Remote Sens. 2020, 12(8), 1337; https://doi.org/10.3390/rs12081337 - 23 Apr 2020

Cited by 5 | Viewed by 3081

Abstract

Fallows are widespread in dryland cropping systems. However, timely information about their spatial extent and location remains scarce. To overcome this lack of information, we propose to classify fractional cover data from Sentinel-2 with biased support vector machines. Fractional cover images describe the land surface in intuitive, biophysical terms, which reduces the spectral variability within the fallow class. Biased support vector machines are a type of one-class classifiers that require labelled data for the class of interest and unlabelled data for the other classes. They allow us to extrapolate in-situ observations collected during flowering to the rest of the growing season to generate large training data sets, thereby reducing the data collection requirements. We tested this approach to monitor fallows in the northern grains region of Australia and showed that the seasonal fallow extent can be mapped with >92% accuracy both during the summer and winter seasons. The summer fallow extent can be accurately mapped as early as mid-December (1–4 months before harvest). The winter fallow extent can be accurately mapped from mid-August (2–4 months before harvest). Our method also detected emergence dates successfully, indicating the near real-time accuracy of our method. We estimated that the extent of fallow fields across the northern grains region of Australia ranged between 50% in winter 2017 and 85% in winter 2019. Our method is scalable, sensor independent and economical to run. As such, it lays the foundations for reconstructing and monitoring the cropping dynamics in Australia. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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13 pages, 5585 KiB

Open AccessArticle

Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

by Jonas E. Böhler, Michael E. Schaepman and Mathias Kneubühler

Remote Sens. 2020, 12(8), 1256; https://doi.org/10.3390/rs12081256 - 16 Apr 2020

Cited by 2 | Viewed by 2367

Abstract

Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of >92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%). Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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28 pages, 2827 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

by Caiwang Zheng, Amr Abd-Elrahman and Vance Whitaker

Remote Sens. 2021, 13(3), 531; https://doi.org/10.3390/rs13030531 - 2 Feb 2021

Cited by 46 | Viewed by 11307

Abstract

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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Other

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17 pages, 904 KiB

Open AccessTechnical Note

A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China

by Haidong Zhang, Lingqing Wang, Ting Tian and Jianghai Yin

Remote Sens. 2021, 13(6), 1221; https://doi.org/10.3390/rs13061221 - 23 Mar 2021

Cited by 86 | Viewed by 9500

Abstract

Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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