Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
3.1
Journals
Horticulturae
Special Issues
Application of Remote Sensing Technology in Orchard Precision Management
share announcement

Application of Remote Sensing Technology in Orchard Precision Management

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 8407

Special Issue Editors

Dr. Xinyang Yu

E-Mail Website
Guest Editor
College of Resources and Environment, Shandong Agricultural University, Taian 271018, China
Interests: vegetation mapping and growth monitoring
Dr. Long Li

E-Mail Website
Guest Editor
School of Public Policy and Management, China University of Mining and Technology, Xuzhou 221116, China
Interests: urban and natural surface mapping; vegetation recovery monitoring; GEOBIA
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To achieve effective orchard management, increase yield stability and gain economic benefits, managers must rely on accurate and timely information regarding an orchard’s environment, fruit physiology, and biochemistry to make informed decisions about planting, irrigation, fertilization, pest control and other related matters. Traditionally, orchard planting information has been primarily obtained through on-site observation, planting records, manual sampling, and laboratory sample testing. However, these methods are time-consuming and labor-intensive, with high costs for large-scale implementation. As a result, they cannot meet the requirements for real-time monitoring and the rapid management of orchard trees over large areas.

After decades of development, remote sensing technology has made significant advancements in the temporal, spatial, spectral, and radiation resolution of data acquisition. It has become an innovative and cost-effective means of identifying and monitoring fruit forests as well as managing orchard planting.

With the seamless integration of remote sensing technologies, including unmanned aerial vehicles and satellite imagery; and emerging tools such as global navigation satellite systems, geographic information systems, big data analytics, and artificial intelligence, precision agriculture is gradually achieving its goal of continuously optimizing operations to increase output while reducing input costs and minimizing losses. Several of these technologies have been implemented in orchard production, ushering in an era of precision management for orchard planting.

This Special Issue welcomes contributions from scholars reporting current achievements and frontier findings that identify the future direction of these cutting-edge applications in orchard planting management. This Special Issue will publish papers dealing with the following main aspects:

  • Remote sensing survey of basic orchard information, e.g., investigation of orchard distribution, orchard age survey, and orchard infrastructure distribution survey.
  • Remote sensing inversion of orchard parameters, including but not limited to the inversion of physiological and biochemical parameters (e.g., LAI, chlorophyll contents of orchard trees, and nutrient content) of orchard trees, and the inversion of environmental parameters (e.g., canopy temperature, soil moisture and nutrients, photosynthetically active radiation).
  • Orchard planting management, e.g., orchard tree growth monitoring (including tree height, crown width, leaf nutrient content, leaf green content, number of flowerings, and number of fruit), Orchard tree water management (vegetation evapotranspiration, water stress pattern), orchard tree nutrient (e.g., nitrogen, phosphorus, potassium) management, and pest management.

We particularly encourage the submission of papers giving experimental evidence of the integration of disciplines such as engineering, mathematics/statistics/physics, and automation. Contributions focusing on agricultural resources management; artificial intelligence; decision support systems; and technologies and solutions for the precision management of orchards, digital and smart agriculture, orchard buildings, and facilities are welcome.

Dr. Xinyang Yu
Dr. Long Li
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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

  • remote sensing
  • orchard precision management
  • orchard distribution
  • orchard age survey
  • orchard infrastructure distribution survey
  • physiological and biochemical parameters of orchard tree
  • environmental parameters
  • orchard tree growth monitoring
  • orchard tree water management
  • orchard tree nutrient management
  • pest management
  • agricultural resources management
  • artificial intelligence
  • decision support systems and technologies and solutions for precision management of orchard
  • digital and smart agriculture
  • soil management and conservation
  • harvest and postharvest technology
  • water conservation storage and utilization
  • orchard buildings and facilities

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

11 pages, 4213 KiB  
Article
Evaluation of Canopy Growth in Rainfed Olive Hedgerows Using UAV-LiDAR
by Susana Cantón-Martínez, Francisco Javier Mesas-Carrascosa, Raúl de la Rosa, Francisca López-Granados, Lorenzo León, Fernando Pérez-Porras, Francisco C. Páez and Jorge Torres-Sánchez
Horticulturae 2024, 10(9), 952; https://doi.org/10.3390/horticulturae10090952 - 6 Sep 2024
Viewed by 583
Abstract
Hedgerow cultivation systems have revolutionized olive growing in recent years because of the mechanization of harvesting. Initially applied under irrigated conditions, its use has now extended to rainfed cultivation. However, there is limited information on the behavior of olive cultivars in hedgerow growing [...] Read more.
Hedgerow cultivation systems have revolutionized olive growing in recent years because of the mechanization of harvesting. Initially applied under irrigated conditions, its use has now extended to rainfed cultivation. However, there is limited information on the behavior of olive cultivars in hedgerow growing systems under rainfed conditions, which is a crucial issue in the context of climate change. To fill this knowledge gap, a rainfed cultivar trial was planted in 2020 in Southern Spain to compare ‘Arbequina’, ‘Arbosana’, ‘Koroneiki’, and ‘Sikitita’, under such growing conditions. One of the most important traits in low-water environments is the canopy growth. Because traditional canopy measurements are costly in terms of time and effort, the use of light detection and ranging (LiDAR) sensor onboard an uncrewed aerial vehicle (UAV) was tested. Statistical analyses of data collected in November 2022 and January 2023 revealed high correlations between UAV-LiDAR metrics and field measurements for height, projected area, and crown volume, based on validation with measurements from 36 trees. These results provide a solid basis for future research and practical applications in rainfed olive growing, while highlighting the potential of UAV-LiDAR technology to characterize tree canopy structure efficiently. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Figure 1

15 pages, 8499 KiB  
Article
Simulation and Analysis of Bidirectional Reflection Factors of Southern Evergreen Fruit Trees Based on 3D Radiative Transfer Model
by Chaofan Hong, Dan Li, Liusheng Han, Xiong Du, Shuisen Chen, Jianbo Qi, Chongyang Wang, Xia Zhou, Boxiong Qin, Hao Jiang, Kai Jia and Zuanxian Su
Horticulturae 2024, 10(8), 790; https://doi.org/10.3390/horticulturae10080790 - 26 Jul 2024
Viewed by 706
Abstract
The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit [...] Read more.
The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Figure 1

16 pages, 4514 KiB  
Article
The Effect of Irrigation on the Vineyard Canopy and Individual Leaf Morphology Evaluated with Proximal Sensing, Colorimetry, and Traditional Morphometry
by Peter Lepej, Dóra Taranyi, Jurij Rakun, Balázs Nagy, Szabina Steckl, György Lukácsy, Nárcisz Mikóczy, Diána Ágnes Nyitrainé Sárdy and Péter Bodor-Pesti
Horticulturae 2024, 10(7), 716; https://doi.org/10.3390/horticulturae10070716 - 5 Jul 2024
Viewed by 836
Abstract
The high number of grapevine (Vitis vinifera L.) cultivars grown world-wide are described and identified according to detailed morphological and morphometric descriptor lists. The grapevine leaf is of utmost importance in characterization, despite its traits being very sensitive to environmental factors. In [...] Read more.
The high number of grapevine (Vitis vinifera L.) cultivars grown world-wide are described and identified according to detailed morphological and morphometric descriptor lists. The grapevine leaf is of utmost importance in characterization, despite its traits being very sensitive to environmental factors. In this study, the effect of irrigation/drought stress on the individual leaf morphology and morphometry of the ‘Hárslevelű’ grapevine (Vitis vinifera L.) cultivar was examined. To verify the effect of the applied irrigation methods (drip and subsoil irrigation) on the plant’s water status, water potential measurements were carried out during the 2022 season. The effect of the applied treatments on the vegetative growth was evaluated according to point quadrat and a multichannel LiDAR analysis in order to describe the width of the canopy area, row volume, and area coverage index. The individual leaf morphology was assessed via traditional morphometry and colorimetry. Our results showed that rainfed plants had a significantly lower stem ψ compared to the drip- and subsoil-irrigated plants at all examined dates. The point quadrat results indicate that the leaf layer number was significantly (p < 0.05) influenced by the position, while the treatment showed no effect on the leaf layer number. The leaf colorimetry showed a difference among the samples, as significant alterations were found in 28 out of the 32 examined color properties. Within the traditional morphometric analysis, 54 traits were evaluated, and 14 of the traits were significantly altered due to the different water management systems. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Graphical abstract

23 pages, 10810 KiB  
Article
Selection of Spectral Parameters and Optimization of Estimation Models for Soil Total Nitrogen Content during Fertilization Period in Apple Orchards
by Zhilin Gao, Wenqian Wang, Hongjia Wang and Ruiyan Li
Horticulturae 2024, 10(4), 358; https://doi.org/10.3390/horticulturae10040358 - 4 Apr 2024
Viewed by 948
Abstract
The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, [...] Read more.
The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, was taken as the experimental area. The optimal method for extracting spectral characteristic bands and screening spectral characteristic indices (SCIs) of soil total nitrogen (TN) for independent and comprehensive fertilization periods was explored. Independent and comprehensive soil TN content estimation models were constructed and optimized for each and the entire fertilization period, respectively. The results show that compared with the correlation coefficient method, stepwise multiple linear regression (SMLR) performs better in extracting hyperspectral characteristic bands of soil TN content. It helps to achieve a higher modeling accuracy, smaller root mean square error (RMSE), and is more conducive to avoiding the influence of multicollinearity of model variables. The sensitive areas of soil TN content in the SCI do not undergo significant changes due to different fertilization periods. Among them, the ratio spectral indices (RSIs) are in the range of 800–900 nm, 1900–1950 nm, and 2200–2300 nm, while the sensitive areas of the difference spectral index (DI) and Normalized difference spectral index (NDSI) are in the range of 1900–1950 nm and 2200–2300 nm. The combination of SCI and characteristic bands significantly improves the prediction accuracy of soil TN estimation models. The independent and comprehensive estimation models for each fertilization period based on the BP (back propagation) neural network optimized by the Mind Evolution Algorithm (MEA-BPNN) can achieve a more stable and accurate estimation of soil TN. Finally, using soil spectral characteristic bands selected through continuum removal (CR) transformation and SMLR, combined with SCI, the model based on the MEA-BPNN (CR-SCI-MEA-BPNN) has the best prediction performance. The modeling determination coefficients R2 for each fertilization period reached 0.94, 0.95, 0.92, and 0.94, respectively, with RMSE of 0.0032, 0.0024, 0.0035, and 0.0027. The R2 and RMSE of the modeling and validation set of the entire fertilization period comprehensive model are 0.899, 0.0038, and 0.89, 0.0041, respectively. The results of this article provide technical support for promoting the timely monitoring of soil TN content and guiding rational fertilization in apple orchards. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Figure 1

12 pages, 588 KiB  
Article
Technical Feasibility Analysis of Advanced Monitoring with a Thermal Camera on an Unmanned Aerial Vehicle and Pressure Chamber for Water Status in Vineyards
by Gonzalo Esteban-Sanchez, Carlos Campillo, David Uriarte and Francisco J. Moral
Horticulturae 2024, 10(3), 305; https://doi.org/10.3390/horticulturae10030305 - 21 Mar 2024
Cited by 1 | Viewed by 1093
Abstract
Water is a limiting factor and to adopt the most appropriate agronomic strategy it is necessary to know the water status. The objective is (i) analysing of the influence of different agronomic treatments on canopy temperature in vineyards with a thermal camera on [...] Read more.
Water is a limiting factor and to adopt the most appropriate agronomic strategy it is necessary to know the water status. The objective is (i) analysing of the influence of different agronomic treatments on canopy temperature in vineyards with a thermal camera on an unmanned aerial vehicle (UAV), (ii) analysing of the influence of different agronomic treatments on vineyard water potentials with a pressure chamber, (iii) advanced technical feasibility analysis of vineyard crop monitoring. The control treatment (T07) in cv. Grenache consisted of applying 30% of reference evapotranspiration (ETo) with irrigation frequency every seven days and seven different treatments were proposed with different irrigation frequencies, pre-bud irrigation, and vine shoot distribution (T03, T15, T7A, T7V, T7P, T00, and T0P). As a result and in conclusion, the use of thermal cameras in UAVs and mid-day stem water potential allows differentiation between irrigated and unirrigated treatments, but no clear differences were shown between irrigation frequencies, pre-irrigation treatment, or vine shoot distribution. Comparing the thermal camera information in UAV and the stem water potential, certain patterns are identified with significant correlation values, the use of thermal cameras for the evaluation of plant water status is recommended, especially to obtain information in large areas. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Figure 1

20 pages, 13353 KiB  
Article
Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards
by Canting Zhang, Xicun Zhu, Meixuan Li, Yuliang Xue, Anran Qin, Guining Gao, Mengxia Wang and Yuanmao Jiang
Horticulturae 2023, 9(10), 1085; https://doi.org/10.3390/horticulturae9101085 - 29 Sep 2023
Cited by 2 | Viewed by 1334
Abstract
Utilizing multi-source remote sensing data fusion to achieve efficient and accurate monitoring of crop nitrogen content is crucial for precise crop management. In this study, an effective integrated method for inverting nitrogen content in apple orchard canopies was proposed based on the fusion [...] Read more.
Utilizing multi-source remote sensing data fusion to achieve efficient and accurate monitoring of crop nitrogen content is crucial for precise crop management. In this study, an effective integrated method for inverting nitrogen content in apple orchard canopies was proposed based on the fusion of ground-space remote sensing data. Firstly, ground hyper-spectral data, unmanned aerial vehicles (UAVs) multi-spectral data, and apple leaf samples were collected from the apple tree canopy. Secondly, the canopy spectral information was extracted, and the hyper-spectral and UAV multi-spectral data were fused using the Convolution Calculation of the Spectral Response Function (SRF-CC). Based on the raw and simulated data, the spectral feature parameters were constructed and screened, and the canopy abundance parameters were constructed using simulated multi-spectral data. Thirdly, a variety of machine-learning models were constructed and verified to identify the optimal inversion model for spatially inverting the canopy nitrogen content (CNC) in apple orchards. The results demonstrated that SRF-CC was an effective method for the fusion of ground-space remote sensing data, and the fitting degree (R2) of raw and simulated data in all bands was higher than 0.70; the absolute values of the correlation coefficients (|R|) between each spectral index and the CNC increased to 0.55–0.68 after data fusion. The XGBoost model established based on the simulated data and canopy abundance parameters was the optimal model for the CNC inversion (R2 = 0.759, RMSE = 0.098, RPD = 1.855), and the distribution of the CNC obtained from the inversion was more consistent with the actual distribution. The findings of this study can provide the theoretical basis and technical support for efficient and non-destructive monitoring of canopy nutrient status in apple orchards. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 9067 KiB  
Review
Management Information Systems for Tree Fruit–2: Design of a Mango Harvest Forecast Engine
by Hari Krishna Dhonju, Thakur Bhattarai, Marcelo H. Amaral, Martina Matzner and Kerry B. Walsh
Horticulturae 2024, 10(3), 301; https://doi.org/10.3390/horticulturae10030301 - 20 Mar 2024
Cited by 1 | Viewed by 1994
Abstract
Spatially enabled yield forecasting is a key component of farm Management Information Systems (MISs) for broadacre grain production, enabling management decisions such as variable rate fertilization. However, such a capability has been lacking for soft (fleshy)-tree-fruit harvest load, with relevant tools for automated [...] Read more.
Spatially enabled yield forecasting is a key component of farm Management Information Systems (MISs) for broadacre grain production, enabling management decisions such as variable rate fertilization. However, such a capability has been lacking for soft (fleshy)-tree-fruit harvest load, with relevant tools for automated assessment having been developed only recently. Such tools include improved estimates of the heat units required for fruit maturation and in-field machine vision for flower and fruit count and fruit sizing. Feedback on the need for and issues in forecasting were documented. A mango ‘harvest forecast engine’ was designed for the forecasting of harvest timing and fruit load, to aid harvest management. Inputs include 15 min interval temperature data per orchard block, weekly manual or machine-vision-derived estimates of flowering, and preharvest manual or machine-vision-derived estimates of fruit load on an orchard block level across the farm. Outputs include predicted optimal harvest time and fruit load, on a per block and per week basis, to inform harvest scheduling. Use cases are provided, including forecast of the order of harvest of blocks within the orchard, management of harvest windows to match harvesting resources such as staff availability, and within block spatial allocation of resources, such as adequate placement of harvest field bin and frost fans. Design requirements for an effective harvest MIS software artefact incorporating the forecast engine are documented, including an integrated database supporting spatial query, data analysis, processing and mapping, an integrated geospatial database for managing of large spatial–temporal datasets, and use of dynamic web map services to enable rapid visualization of large datasets. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Orchard Precision Management)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop