Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (10 August 2022) | Viewed by 48391

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

Special issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture book cover image

Share This Special Issue

Special Issue Editors

Dr. Jiyul Chang

E-Mail Website
Guest Editor

Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57007, USA
Interests: site-specific fertilizer management; using remote sensing data (satellite and drone images) for crop managements; using variouse spatial data (yield monitoring data, elevation data, soil data, RS data, soil test data) to describe spatial variability of production fields
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sigfredo Fuentes

E-Mail Website
Guest Editor

Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

When adopting remote sensing techniques in precision agriculture, there are two main areas to be considered, from data acquisition to data analyses.

Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multispectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth, and water status of crops to yield estimations. However, accurate detection of specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application.

In data analysis, the volume and complexity of data formats obtained using the latest technologies in remote sensing (e.g., a cube of data for hyperspectral imagery) demands complex data processing systems and data analysis using multiple inputs to estimate specific categorical or numerical targets. New and emerging methodologies within artificial intelligence, such as machine learning and deep learning, have enabled us to deal with these increasing data volumes and analysis complexity.

Therefore, this Special Issue mainly focuses on i) advanced methodologies for remotely sensed data collected by different types of sensors and platforms for precision agriculture and ii) the implementation of various kinds of sensors for specific targets in precision agriculture. The submissions need to cover, but are not limited to the following topics:

Advantages of hyperspectral sensors to detect biotic and abiotic plant stresses.
Multispectral sensors for specific targets in precision agricultural management.
Implementation of artificial intelligence, such as machine and deep learning modeling to classifying images or process data to discriminate crop plants under different stress conditions.
Spectral indices and models for specific targets in precision agricultural management.
Advantages and disadvantages of the use of very high spatial resolution images by different platforms for precision agriculture.
Other remote sensing sensors are used through different platforms, such as electronic noses (e-noses), Lidar, and infrared thermography, among others.

Dr. Jiyul Chang
Dr. Sigfredo Fuentes
Guest Editor

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. Sensors 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 2600 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

Multi-spectral sensors
Hyper-spectral sensor
Unmanned Aerial Systems (UAS) or vehicles (UAV or drones)
Spectral indices
Gas sensor technologies (electronic noses)
Precision agriculture
Digital Agriculture
Deep learning
Remote sensing
Crop plants
Diseases
Modeling strategies

Published Papers (12 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research

3 pages, 180 KiB

Open AccessEditorial

Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture

by Sigfredo Fuentes and Jiyul Chang

Sensors 2022, 22(20), 7898; https://doi.org/10.3390/s22207898 - 17 Oct 2022

Cited by 3 | Viewed by 1716

Abstract

When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies [...] Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

Research

Jump to: Editorial

19 pages, 2647 KiB

Open AccessArticle

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

by Alexis Pang, Melissa W L Chang and Yang Chen

Sensors 2022, 22(3), 717; https://doi.org/10.3390/s22030717 - 18 Jan 2022

Cited by 22 | Viewed by 3118

Abstract

Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R² = 0.86, RMSE = 0.18 t ha⁻¹). RF models for individual paddocks in VIC (R² = 0.89, RMSE = 0.15 t ha⁻¹) and NSW (R² = 0.87, RMSE = 0.07 t ha⁻¹) performed well, but moderate performance was seen for SA (R² = 0.45, RMSE = 0.25 t ha⁻¹). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

24 pages, 7214 KiB

Open AccessEditor’s ChoiceArticle

Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

by Prakriti Sharma, Larry Leigh, Jiyul Chang, Maitiniyazi Maimaitijiang and Melanie Caffé

Sensors 2022, 22(2), 601; https://doi.org/10.3390/s22020601 - 13 Jan 2022

Cited by 32 | Viewed by 4924

Abstract

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R² = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R² = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

12 pages, 1999 KiB

Open AccessArticle

Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

by Xinyang Yu, Chunyan Chang, Jiaxuan Song, Yuping Zhuge and Ailing Wang

Sensors 2022, 22(2), 546; https://doi.org/10.3390/s22020546 - 11 Jan 2022

Cited by 18 | Viewed by 8958

Abstract

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R²) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R², RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

20 pages, 1819 KiB

Open AccessArticle

Comparison of Selected Dimensionality Reduction Methods for Detection of Root-Knot Nematode Infestations in Potato Tubers Using Hyperspectral Imaging

by Janez Lapajne, Matej Knapič and Uroš Žibrat

Sensors 2022, 22(1), 367; https://doi.org/10.3390/s22010367 - 4 Jan 2022

Cited by 12 | Viewed by 2577

Abstract

Hyperspectral imaging is a popular tool used for non-invasive plant disease detection. Data acquired with it usually consist of many correlated features; hence most of the acquired information is redundant. Dimensionality reduction methods are used to transform the data sets from high-dimensional, to low-dimensional (in this study to one or a few features). We have chosen six dimensionality reduction methods (partial least squares, linear discriminant analysis, principal component analysis, RandomForest, ReliefF, and Extreme gradient boosting) and tested their efficacy on a hyperspectral data set of potato tubers. The extracted or selected features were pipelined to support vector machine classifier and evaluated. Tubers were divided into two groups, healthy and infested with Meloidogyne luci. The results show that all dimensionality reduction methods enabled successful identification of inoculated tubers. The best and most consistent results were obtained using linear discriminant analysis, with 100% accuracy in both potato tuber inside and outside images. Classification success was generally higher in the outside data set, than in the inside. Nevertheless, accuracy was in all cases above 0.6. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

19 pages, 5554 KiB

Open AccessArticle

Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

by Ziheng Feng, Li Song, Jianzhao Duan, Li He, Yanyan Zhang, Yongkang Wei and Wei Feng

Sensors 2022, 22(1), 31; https://doi.org/10.3390/s22010031 - 22 Dec 2021

Cited by 32 | Viewed by 3736

Abstract

Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R² for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

19 pages, 6182 KiB

Open AccessArticle

Hyperspectral Estimation of Winter Wheat Leaf Area Index Based on Continuous Wavelet Transform and Fractional Order Differentiation

by Changchun Li, Yilin Wang, Chunyan Ma, Fan Ding, Yacong Li, Weinan Chen, Jingbo Li and Zhen Xiao

Sensors 2021, 21(24), 8497; https://doi.org/10.3390/s21248497 - 20 Dec 2021

Cited by 16 | Viewed by 2933

Abstract

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R² of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

16 pages, 11596 KiB

Open AccessArticle

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model

by Baohua Yang, Yue Zhu and Shuaijun Zhou

Sensors 2021, 21(20), 6826; https://doi.org/10.3390/s21206826 - 14 Oct 2021

Cited by 22 | Viewed by 2306

Abstract

The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has low accuracy and poor real-time performance. To overcome this gap, first, four-channel fusion images, including RGB and DSM (digital surface model), as well as RGB and ExG (excess green), were constructed based on the RGB image acquired from unmanned aerial vehicle (UAV). Second, a Mobile U-Net model that combined a lightweight neural network with a depthwise separable convolution and U-Net model was proposed. Finally, three data sets (RGB, RGB + DSM and RGB + ExG) were used to train, verify, test and evaluate the proposed model. The results of the experiment showed that the overall accuracy of lodging recognition based on RGB + DSM reached 88.99%, which is 11.8% higher than that of original RGB and 6.2% higher than that of RGB + ExG. In addition, our proposed model was superior to typical deep learning frameworks in terms of model parameters, processing speed and segmentation accuracy. The optimized Mobile U-Net model reached 9.49 million parameters, which was 27.3% and 33.3% faster than the FCN and U-Net models, respectively. Furthermore, for RGB + DSM wheat lodging extraction, the overall accuracy of Mobile U-Net was improved by 24.3% and 15.3% compared with FCN and U-Net, respectively. Therefore, the Mobile U-Net model using RGB + DSM could extract wheat lodging with higher accuracy, fewer parameters and stronger robustness. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

17 pages, 3098 KiB

Open AccessArticle

Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat

by Jie Jiang, Cuicun Wang, Hui Wang, Zhaopeng Fu, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao and Xiaojun Liu

Sensors 2021, 21(16), 5579; https://doi.org/10.3390/s21165579 - 19 Aug 2021

Cited by 12 | Viewed by 3060

Abstract

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha⁻¹) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

19 pages, 1128 KiB

Open AccessArticle

Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

by Gabriel Silva de Oliveira, José Marcato Junior, Caio Polidoro, Lucas Prado Osco, Henrique Siqueira, Lucas Rodrigues, Liana Jank, Sanzio Barrios, Cacilda Valle, Rosângela Simeão, Camilo Carromeu, Eloise Silveira, Lúcio André de Castro Jorge, Wesley Gonçalves, Mateus Santos and Edson Matsubara

Sensors 2021, 21(12), 3971; https://doi.org/10.3390/s21123971 - 9 Jun 2021

Cited by 17 | Viewed by 3270

Abstract

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha

^{- 1}

for LDMY and from 413.07 to 506.56 kg·ha

^{- 1}

for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

22 pages, 4143 KiB

Open AccessArticle

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

by Jayan Wijesingha, Supriya Dayananda, Michael Wachendorf and Thomas Astor

Sensors 2021, 21(8), 2886; https://doi.org/10.3390/s21082886 - 20 Apr 2021

Cited by 10 | Viewed by 3062

Abstract

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Figure 1

20 pages, 8500 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery

by Javier Campos, Francisco García-Ruíz and Emilio Gil

Sensors 2021, 21(7), 2363; https://doi.org/10.3390/s21072363 - 29 Mar 2021

Cited by 29 | Viewed by 4288

Abstract

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

► Show Figures

Journal Menu

Journal Browser

Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (12 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI