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Application of Remote Sensing Technology in Orchard Precision Management

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 4006

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Special Issue Editors

Dr. Xinyang Yu

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Guest Editor

College of Resources and Environment, Shandong Agricultural University, Taian 271018, China
Interests: vegetation mapping and growth monitoring

Dr. Long Li

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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
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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

Published Papers (4 papers)

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Research

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23 pages, 10810 KiB

Open AccessArticle

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 506

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, 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)

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12 pages, 588 KiB

Open AccessArticle

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

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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 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 (ET_o) 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)

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20 pages, 13353 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 956

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 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)

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Review

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25 pages, 9067 KiB

Open AccessFeature PaperReview

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

Viewed by 1316

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 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)

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