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Remote Sensing and Decision Support for Precision Orchard Production

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 44175

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Prof. Dr. Guijun Yang

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National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, 11 Middle Road, Haidian District, Beijing 100097, China
Interests: remote sensing; agronomic modelling; UAV-based sensors; precision farming
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhenhong Li

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College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China
Interests: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture
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Dr. James Taylor

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National Research Institute of Science and Technology for Environment and Agriculture, UMR ITAP, Montpellier, Languedoc-Roussillon, France
Interests: precision farming; crop management; viticulture; spatial statistics; soil mapping

Prof. Dr. Raffaele Casa

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Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University Via San Camillo de Lellis, 01100 Viterbo, Italy
Interests: precision agriculture; remote sensing; agronomic modelling; data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Yang

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1. National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 10089, China
2. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: orchard monitoring; crop phenotyping; LiDAR; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart orchard is a new generation of orchard production system relying on smart sensing, smart decision, and smart intervention, targeted to optimize agronomic inputs and resilience, and improve yield/quality and production efficiency. The evolution of remote sensing techniques has opened new perspectives for supporting orchard production and management. The enhanced spectral, spatial, and temporal resolution of various sensors (i.e., multi/hyperspectral, LiDAR, thermal, and fluorescence,) on board platforms (spaceborne, airborne, UAVs, vehicle, robots, and backpack) offers unprecedented possibilities for efficient orchard monitoring at different application scales and purposes. The link of the remote sensing technology and orchard agronomic model is expected to support intelligent decisions regarding fertilizer, water, and chemical inputs, optimizing and predicting fruit yield and quality. To accelerate the transition from traditional orchard production to smart orchard, this Special Issue aims at providing the state-of-the-art of remote sensing techniques for orchard management, with a special focus on operational applications targeted to the needs of the final users, that is, fruit producers, farmer associations, and regulating authorities.

This Special Issue invites contributions on the following:

Innovative sensors and technologies for smart orchard sensing;
Novel intelligent data fusion methods for orchard understanding and learning;
Orchard modelling and decision system in smart orchard application.

Submissions are encouraged to cover a broad range of topics that may include, but are not limited to, the following activities:

Plant healthy sensor development IoT for orchard management
Multi-platform data fusion on smart orchard
Empirical and physical model of remote sensing
Quantitative inversion of orchard physicochemical parameters
Orchard structure parameters estimation
Nutrition/water stress diagnosis
Pest/disease monitoring and prediction orchard process-based agronomic model
Intelligent decision-making tools
Traceability system based on smart sensing
Orchard yield and quality estimation
Remote sensing and data assimilation
Big data and systems for smart orchard
Social–economical assessment for sensing technology

Prof. Guijun Yang
Prof. Zhenhong Li
Dr. James Taylor
Prof. Raffaele Casa
Dr. Hao Yang
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

Satellite UAV–UGV remote sensing
Multi-spectral/hyper-spectral
LiDAR/TLS
Thermal
Data fusion
Deep learning
Artificial intelligence (AI)
Nutrition stress
Water stress
Pest and diseases
Yield mapping and prediction
Fruit quality prediction
Orchard pruning
Orchard 3D model
Process-based crop modeling
Structural–functional model
Radiative transfer model
Plant growth and health
Orchard decision making
Multi-GNSS
Precision orchard spraying
Traceability system
Social–economical assessment

Published Papers (8 papers)

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Research

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16 pages, 2524 KiB

Open AccessArticle

Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods

by Na Ta, Qingrui Chang and Youming Zhang

Remote Sens. 2021, 13(19), 3902; https://doi.org/10.3390/rs13193902 - 29 Sep 2021

Cited by 22 | Viewed by 3167

Abstract

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SD_r − SD_b)/ (SD_r + SD_b) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R² values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R² value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R² = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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18 pages, 6939 KiB

Open AccessArticle

Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries

by Xueyuan Bai, Zhenhai Li, Wei Li, Yu Zhao, Meixuan Li, Hongyan Chen, Shaochong Wei, Yuanmao Jiang, Guijun Yang and Xicun Zhu

Remote Sens. 2021, 13(16), 3073; https://doi.org/10.3390/rs13163073 - 05 Aug 2021

Cited by 19 | Viewed by 3134

Abstract

Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (

\sum VIs

)-based random forest (

{RF}_{\sum VI}

) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1)

\sum NDVI

was the optimal predictor to construct an RF model for apple fruit yield, and the R², RMSE, and RPD values of the

{RF}_{\sum NDVI}

model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASA_SR model (R² = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASA_NDVI model and the CASA_Average model (R², RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the

{RF}_{\sum NDVI}

model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASA_SR model (RPD = 1.53). The results obtained from this study indicated the potential of the

{RF}_{\sum NDVI}

model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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18 pages, 6987 KiB

Open AccessArticle

Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data

by Fa Zhao, Guijun Yang, Xiaodong Yang, Haiyan Cen, Yaohui Zhu, Shaoyu Han, Hao Yang, Yong He and Chunjiang Zhao

Remote Sens. 2021, 13(9), 1836; https://doi.org/10.3390/rs13091836 - 08 May 2021

Cited by 14 | Viewed by 2869

Abstract

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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18 pages, 4334 KiB

Open AccessArticle

Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

by Yaohui Zhu, Guijun Yang, Hao Yang, Fa Zhao, Shaoyu Han, Riqiang Chen, Chengjian Zhang, Xiaodong Yang, Miao Liu, Jinpeng Cheng and Chunjiang Zhao

Remote Sens. 2021, 13(9), 1630; https://doi.org/10.3390/rs13091630 - 21 Apr 2021

Cited by 10 | Viewed by 2711

Abstract

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R²), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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18 pages, 5602 KiB

Open AccessArticle

Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

by Chengjian Zhang, Guijun Yang, Youyi Jiang, Bo Xu, Xiao Li, Yaohui Zhu, Lei Lei, Riqiang Chen, Zhen Dong and Hao Yang

Remote Sens. 2020, 12(21), 3592; https://doi.org/10.3390/rs12213592 - 02 Nov 2020

Cited by 32 | Viewed by 4160

Abstract

The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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22 pages, 14140 KiB

Open AccessArticle

Identification of Apple Orchard Planting Year Based on Spatiotemporally Fused Satellite Images and Clustering Analysis of Foliage Phenophase

by Yaohui Zhu, Guijun Yang, Hao Yang, Jintao Wu, Lei Lei, Fa Zhao, Lingling Fan and Chunjiang Zhao

Remote Sens. 2020, 12(7), 1199; https://doi.org/10.3390/rs12071199 - 08 Apr 2020

Cited by 28 | Viewed by 6315

Abstract

The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual planting year based on textural or structural characteristics. Therefore, the monitoring method provided in this paper is not to monitor the growing year positively from the planting of orchard seedlings but to use time series remote sensing data to reverse determine the continuous growth age of each existing orchard. The city of Qixia, Shandong Province, China, was used as a case study. Firstly, the spatial distribution of apple orchards was accurately extracted using the Sentinel-2 normalized difference vegetation index (NDVI) spatiotemporally fused images and phenological vegetation information. Secondly, using region of interest (ROI) data for different vegetation types obtained from a field survey, NDVI time series were extracted from the Sentinel-2 NDVI spatiotemporally fused image. Among them, three characteristic phenological periods were selected, and the NDVI time series for apple orchards was used as a template to extract the apple orchard distribution area from 2000 to 2017. Then, the distribution area of apple orchards was defined as the area of interest in the planting year, combined with the Landsat NDVI time series image composed of three characteristic phenological periods each year from 2000 to 2017, and the apple orchard phenological curve. Subsequently, a Euclidean distance (ED) method was used to calculate the distribution area of apple orchards for each year between 2000 and 2017. Finally, a pixel-by-pixel inverse time series calculation method was used to obtain the planting year of apple orchards in the study area. This study provides a new way to accurately identify the planting year of apple orchards using satellite remote sensing images. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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21 pages, 5413 KiB

Open AccessArticle

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

by Xinyu Dong, Zhichao Zhang, Ruiyang Yu, Qingjiu Tian and Xicun Zhu

Remote Sens. 2020, 12(1), 133; https://doi.org/10.3390/rs12010133 - 01 Jan 2020

Cited by 50 | Viewed by 5974

Abstract

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m² and 0.48 m² were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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Review

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34 pages, 3883 KiB

Open AccessReview

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

by Ning Zhang, Guijun Yang, Yuchun Pan, Xiaodong Yang, Liping Chen and Chunjiang Zhao

Remote Sens. 2020, 12(19), 3188; https://doi.org/10.3390/rs12193188 - 29 Sep 2020

Cited by 116 | Viewed by 14115

Abstract

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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