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Remote Sensing for Precision Agriculture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 67079

Special Issue Editors


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Guest Editor
School of Computing, Tokyo Institute of Technology, Tokyo, Japan
Interests: remote sensing; image processing; machine learning; phenotyping; precision agriculture

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Guest Editor
Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Building 085B, M/S 74R316C, Berkeley, CA, USA
Interests: signal and image processing; machine learning for remote sensing; multimodal data integration; hyperspectral data analysis; remote sensing for precision agriculture
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
GIPSA-Lab, Grenoble Institute of Technology, 38402 Saint Martin d'Hères, France
Interests: remote sensing; image processing; signal processing; machine learning; mathematical morphology; data fusion; multivariate data analysis; hyperspectral imaging; pansharpening
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, significant technological advances in precision agriculture have allowed farmers to improve crop management and productivity. At the same time, the rapid change in food demand as well as the impact that industrialized farming has on the environment have provided ground for the development of more optimized and sustainable practices.

In such a context, remote sensing plays a critical role by providing technology as well as methodologies for crop management and resources monitoring (e.g., water).

Data derived by imaging sensors, including multi-/hyper-spectral, fluorescence, thermal, LiDAR, and SAR, as well as the availability of complementary data (e.g., weather forecasting, soil information derived by sensor networks), provide an unprecedented amount of information for which systems based on multi-source and multi-modal data analysis are necessary to extract intelligible information that can be used by farmers. Additionally, the recent advent of UAVs and lightweight sensors provides us with aerial images with high spatial, spectral, and temporal resolutions. The unique resolutions would allow us to investigate, retrieve, and monitor plant physiological and phenological properties.

This Special Issue is aimed at a global research community involved in data analysis, sensor development, and data acquisition for precision agriculture. As such, it is open to anyone researching in the field of precision agriculture. Specific topics include but are not limited to the folllowing:

  • Plant and culture mapping;
  • Vegetation health monitoring;
  • Species detection (e.g., illicit/invasive plants);
  • Agricultural crop assessment;
  • Yield prediction and quality;
  • Phenotyping estimation;
  • Plant disease detection;
  • Model-based analysis (e.g., by considering 3D plant models);
  • Crop mapping based on multimodal acquisitions (e.g., multi/hyperspectral, thermal, LiDAR point clouds, fluorescence, and SAR imaging);
  • Time-series analysis for agriculture monitoring;
  • In-situ remote sensing measurements (e.g., robotic vision).

Dr. Kuniaki Uto
Dr. Nicola Falco
Dr. Mauro Dalla Mura
Guest Editors

Manuscript Submission Information

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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

  • precision agriculture
  • plant and culture mapping
  • vegetation health monitoring
  • species detection
  • agricultural crop assessment

Published Papers (15 papers)

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22 pages, 5682 KiB  
Article
Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data
by Belen Franch, Alberto San Bautista, David Fita, Constanza Rubio, Daniel Tarrazó-Serrano, Antonio Sánchez, Sergii Skakun, Eric Vermote, Inbal Becker-Reshef and Antonio Uris
Remote Sens. 2021, 13(20), 4095; https://doi.org/10.3390/rs13204095 - 13 Oct 2021
Cited by 17 | Viewed by 4652
Abstract
Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). [...] Read more.
Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22–25, while the harvesting date was September 15–17 for Bomba and October 5–8 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3–7 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R2 for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R2 for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t×ha−1 (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t×ha−1; 3.48% at 20 m) and 0.218 t×ha−1 (MAPE 5.82%) for Bomba (0.223 t×ha−1; 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model’s MAE is 0.176 t×ha−1 (MAPE 2.61%) for JSendra and 0.142 t×ha−1 (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40–50 days after sowing) to achieve high yields. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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21 pages, 8036 KiB  
Article
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection
by Monica F. Danilevicz, Philipp E. Bayer, Farid Boussaid, Mohammed Bennamoun and David Edwards
Remote Sens. 2021, 13(19), 3976; https://doi.org/10.3390/rs13193976 - 4 Oct 2021
Cited by 32 | Viewed by 4421
Abstract
Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to [...] Read more.
Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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28 pages, 23123 KiB  
Article
Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture
by Rui Xu, Changying Li and Sergio Bernardes
Remote Sens. 2021, 13(17), 3517; https://doi.org/10.3390/rs13173517 - 4 Sep 2021
Cited by 16 | Viewed by 4345
Abstract
Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that [...] Read more.
Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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21 pages, 6693 KiB  
Article
Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation
by Jingshan Lu, Jan U. H. Eitel, Jyoti S. Jennewein, Jie Zhu, Hengbiao Zheng, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao and Yongchao Tian
Remote Sens. 2021, 13(17), 3502; https://doi.org/10.3390/rs13173502 - 3 Sep 2021
Cited by 8 | Viewed by 2430
Abstract
Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological [...] Read more.
Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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19 pages, 5582 KiB  
Article
Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices
by Naichen Xing, Wenjiang Huang, Huichun Ye, Yu Ren and Qiaoyun Xie
Remote Sens. 2021, 13(16), 3175; https://doi.org/10.3390/rs13163175 - 11 Aug 2021
Cited by 5 | Viewed by 2222
Abstract
Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, [...] Read more.
Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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21 pages, 7525 KiB  
Article
DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images
by Jehan-Antoine Vayssade, Jean-Noël Paoli, Christelle Gée and Gawain Jones
Remote Sens. 2021, 13(12), 2261; https://doi.org/10.3390/rs13122261 - 9 Jun 2021
Cited by 7 | Viewed by 4189
Abstract
The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form [...] Read more.
The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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21 pages, 8465 KiB  
Article
Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV
by Romina de Souza, Claudia Buchhart, Kurt Heil, Jürgen Plass, Francisco M. Padilla and Urs Schmidhalter
Remote Sens. 2021, 13(9), 1691; https://doi.org/10.3390/rs13091691 - 27 Apr 2021
Cited by 16 | Viewed by 3148
Abstract
Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the [...] Read more.
Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny versus overcast) on several vegetation indices (VI) calculated from two active sensors (the Crop Circle ACS-470 and Greenseeker RT100), two passive sensors (the hyperspectral bidirectional passive spectrometer and HandySpec Field sensor), and images taken from an unmanned aerial vehicle (UAV). The experimental work was conducted in a wheat crop in south-west Germany, with eight nitrogen (N) application treatments. Optical sensor measurements were made throughout the vegetative growth period on different dates in 2019 at 9:00, 14:00, and 16:00 solar time to evaluate the effect of time of day, and on a sunny and overcast day only at 9:00 h to evaluate the influence of sky conditions on different vegetation indices. For most vegetation indices evaluated, there were significant differences between paired time measurements, regardless of the sensor and day of measurement. The smallest differences between measurement times were found between measurements at 14:00 and 16:00 h, and they were observed for the vehicle-carried and the handheld hyperspectral passive sensor being lower than 2% and 4%, respectively, for the indices NIR/Red edge ratio, Red edge inflection point (REIP), and the water index. Differences were lower than 5% for the vehicle-carried active sensors Crop Circle ACS-470 (indices NIR/Red edge and NIR/Red ratios, and NDVI) and Greenseeker RT100 (index NDVI). The most stable indices over measurement times were the NIR/Red edge ratio, water index, and REIP index, regardless of the sensor used. The most considerable differences between measurement times were found for the simple ratios NIR/Red and NIR/Green. For measurements made on a sunny and overcast day, the most stable were the indices NIR/Red edge ratio, water index, and REIP. In practical terms, these results confirm that passive and active sensors could be used to measure on-farm at any time of day from 9:00 to 16:00 h by choosing optimized indices. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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15 pages, 2701 KiB  
Article
Using Multi-Angular Hyperspectral Data to Estimate the Vertical Distribution of Leaf Chlorophyll Content in Wheat
by Bin Wu, Wenjiang Huang, Huichun Ye, Peilei Luo, Yu Ren and Weiping Kong
Remote Sens. 2021, 13(8), 1501; https://doi.org/10.3390/rs13081501 - 13 Apr 2021
Cited by 16 | Viewed by 2441
Abstract
Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution [...] Read more.
Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R2) = 0.69, root mean square error (RMSE) = 4.80 ug/cm2, MCARI/OSAVI with R2 = 0.73, RMSE = 4.17 ug/cm2), VZA 10° and 30° for the middle-layer LCC (TCARI with R2 = 0.17, RMSE = 4.81 ug/cm2, MCARI/OSAVI with R2 = 0.17, RMSE = 4.76 ug/cm2), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R2 = 0.40, RMSE = 6.29 ug/cm2, MCARI/OSAVI with R2 = 0.40, RMSE = 6.36 ug/cm2). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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25 pages, 2424 KiB  
Article
Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series
by Florian Mouret, Mohanad Albughdadi, Sylvie Duthoit, Denis Kouamé, Guillaume Rieu and Jean-Yves Tourneret
Remote Sens. 2021, 13(5), 956; https://doi.org/10.3390/rs13050956 - 4 Mar 2021
Cited by 20 | Viewed by 4044
Abstract
This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing [...] Read more.
This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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21 pages, 2829 KiB  
Article
Early Detection of Ganoderma boninense in Oil Palm Seedlings Using Support Vector Machines
by Aiman Nabilah Noor Azmi, Siti Khairunniza Bejo, Mahirah Jahari, Farrah Melissa Muharam, Ian Yule and Nur Azuan Husin
Remote Sens. 2020, 12(23), 3920; https://doi.org/10.3390/rs12233920 - 29 Nov 2020
Cited by 28 | Viewed by 5828
Abstract
Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects [...] Read more.
Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects both mature trees and seedlings. The current practice of detection still depends on manual inspection by a human expert every two weeks. This study aimed to detect early G. boninense infections using visible-near infrared (VIS-NIR) hyperspectral images where there are no BSR symptoms present. Twenty-eight samples of oil palm seedlings at five months old were used whereby 15 of them were inoculated with the G. boninense pathogen. Five months later, spectral reflectance oil palm leaflets taken from fronds 1 (F1) and 2 (F2) were obtained from the VIS-NIR hyperspectral images. The significant bands were identified based on the high separation between uninoculated (U) and inoculated (I) seedlings. The results indicate that the differences were evidently seen in the NIR spectrum. The bands were later used as input parameters for the development of Support Vector Machine (SVM) classification models, and these bands were optimized according to the classification accuracy achieved by the classifiers. It was observed that the U and I seedlings were excellently classified with 100% accuracy using 35 bands and 18 bands of F1. However, the combination of F1 and F2 (F12) gave better accuracy than F2 and almost similar to F1 for specific classifiers. This finding will provide an advantage when using aerial images where there is no need to separate F1 and F2 during the data pre-processing stage. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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27 pages, 9172 KiB  
Article
Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression
by Yuanyuan Fu, Guijun Yang, Zhenhai Li, Xiaoyu Song, Zhenhong Li, Xingang Xu, Pei Wang and Chunjiang Zhao
Remote Sens. 2020, 12(22), 3778; https://doi.org/10.3390/rs12223778 - 18 Nov 2020
Cited by 53 | Viewed by 3157
Abstract
Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. [...] Read more.
Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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20 pages, 9631 KiB  
Article
Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data
by Gangqiang An, Minfeng Xing, Binbin He, Chunhua Liao, Xiaodong Huang, Jiali Shang and Haiqi Kang
Remote Sens. 2020, 12(18), 3104; https://doi.org/10.3390/rs12183104 - 22 Sep 2020
Cited by 45 | Viewed by 5182
Abstract
Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, [...] Read more.
Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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18 pages, 7596 KiB  
Article
Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring
by Jian Zhang, Chufeng Wang, Chenghai Yang, Tianjin Xie, Zhao Jiang, Tao Hu, Zhibang Luo, Guangsheng Zhou and Jing Xie
Remote Sens. 2020, 12(7), 1207; https://doi.org/10.3390/rs12071207 - 8 Apr 2020
Cited by 30 | Viewed by 4812
Abstract
The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled [...] Read more.
The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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17 pages, 4369 KiB  
Article
Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan
by Emal Wali, Masahiro Tasumi and Masao Moriyama
Remote Sens. 2020, 12(1), 189; https://doi.org/10.3390/rs12010189 - 5 Jan 2020
Cited by 23 | Viewed by 4760
Abstract
This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from [...] Read more.
This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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16 pages, 7089 KiB  
Technical Note
GRID: A Python Package for Field Plot Phenotyping Using Aerial Images
by Chunpeng James Chen and Zhiwu Zhang
Remote Sens. 2020, 12(11), 1697; https://doi.org/10.3390/rs12111697 - 26 May 2020
Cited by 11 | Viewed by 9090
Abstract
Aerial imagery has the potential to advance high-throughput phenotyping for agricultural field experiments. This potential is currently limited by the difficulties of identifying pixels of interest (POI) and performing plot segmentation due to the required intensive manual operations. We developed a Python package, [...] Read more.
Aerial imagery has the potential to advance high-throughput phenotyping for agricultural field experiments. This potential is currently limited by the difficulties of identifying pixels of interest (POI) and performing plot segmentation due to the required intensive manual operations. We developed a Python package, GRID (GReenfield Image Decoder), to overcome this limitation. With pixel-wise K-means cluster analysis, users can specify the number of clusters and choose the clusters representing POI. The plot grid patterns are automatically recognized by the POI distribution. The local optima of POI are initialized as the plot centers, which can also be manually modified for deletion, addition, or relocation. The segmentation of POI around the plot centers is initialized by automated, intelligent agents to define plot boundaries. A plot intelligent agent negotiates with neighboring agents based on plot size and POI distributions. The negotiation can be refined by weighting more on either plot size or POI density. All adjustments are operated in a graphical user interface with real-time previews of outcomes so that users can refine segmentation results based on their knowledge of the fields. The final results are saved in text and image files. The text files include plot rows and columns, plot size, and total plot POI. The image files include displays of clusters, POI, and segmented plots. With GRID, users are completely liberated from the labor-intensive task of manually drawing plot lines or polygons. The supervised automation with GRID is expected to enhance the efficiency of agricultural field experiments. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
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