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Drones
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Advances of UAV in Precision Agriculture
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Advances of UAV in Precision Agriculture

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 19499

Special Issue Editors

Dr. Görres Grenzdörffer

E-Mail Website
Guest Editor
Chair for Geodesy and Geoinformatics, Faculty for Agriculture and Environmental Sciences, Rostock University, Rostock, Germany
Interests: precision agriculture; remote sensing; phenology; photogrammetry
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Chen

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: bio-inspired perception and embodied intelligent control of unmanned systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

At present, intelligent agricultural unmanned systems have covered space (navigation, remote sensing, meteorological, and communication satellites), air (plant-protection UAVs, remote sensing and mapping UAVs, long-endurance solar-powered UAVs, long-endurance airships, and bionic flying robots), ground (unmanned farming/harvesting machinery, biomass energy system, soil improved bionic robot, and unmanned animal-husbandry robot), and water (unmanned underwater vehicle, underwater operation robot, and unmanned aquaculture system): four spatial dimensions with broad development prospects. Establishing an agricultural integrated space–air–ground–water cooperation and precision operation system based on the closed-loop control of large systems, studying the intelligent sensing and control technology of intelligent agricultural unmanned systems and establishing application demonstration bases all over the world play an important role in supporting leaping developments regarding automotive operations, intelligent operations, unmanned operations, and cluster operations of intelligent agricultural machinery and equipment. It is also of great significance to realize the short-term goal “unmanned farming” and the long-term goal “unmanned agriculture” of world agricultural modernization.

This Special Issue is aimed at publishing state-of-the-art advances and the latest achievements of UAV technologies in precision agriculture which fully relates to the journal scope.

Articles covering but not limited to recent research on the following topics are invited to this Special Issue:

  • Agricultural information integrated space-air-ground-water remote sensing and monitoring network (satellites, UAVs, UGVs, USVs and UUVs) and multi-source data fusion for agricultural applications;
  • Unmanned agricultural intelligent sensing and control system, intelligent agricultural equipment, and autonomous systems for agricultural machinery field operations;
  • Unmanned simultaneous localization and mapping, and sensing of unmanned robots in agriculture;
  • Unmanned agricultural robots guidance (path planning), navigation and control;
  • Bio-inspired swarm intelligence and multi-agent system cooperative control;
  • Unmanned soil moisture and crop phenotype detection, hyper-spectral sensing, and quantitative inversion;
  • Spray or seeding Drones for agricultural applications (Fertilization, Crop Protection);
  • Drones for Precision Agriculture, e.g., nutrients analysis, insect infestation analysis, fungus infestation analysis, snails attack mapping, soil quality and soil compaction mapping, drainage system analysis, Harvest prediction;
  • Bionic flying robots, and the flying robot with soft grasping manipulator;
  • Drones in / for green house.

Dr. Görres Grenzdörffer
Dr. Jian Chen
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. Drones 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 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agricultural UAV
  • guidance, navigation and control
  • SLAM
  • swarm intelligence
  • remote sensing
  • crop phenotype awareness
  • crop and/or water stress assessment
  • drones for agricultural applications
  • drones for precision agriculture
  • precision viticulture

Published Papers (6 papers)

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Research

Jump to: Review

20 pages, 3010 KiB  
Article
Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology
by Sándor Zsebő, László Bede, Gábor Kukorelli, István Mihály Kulmány, Gábor Milics, Dávid Stencinger, Gergely Teschner, Zoltán Varga, Viktória Vona and Attila József Kovács
Drones 2024, 8(3), 88; https://doi.org/10.3390/drones8030088 - 05 Mar 2024
Viewed by 1270
Abstract
This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving [...] Read more.
This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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20 pages, 16424 KiB  
Article
Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model
by Zhao Liu, Huapeng Li, Xiaohui Ding, Xinyuan Cao, Hui Chen and Shuqing Zhang
Drones 2023, 7(9), 586; https://doi.org/10.3390/drones7090586 - 19 Sep 2023
Cited by 1 | Viewed by 1279
Abstract
Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, [...] Read more.
Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R2 value was 0.704, the RMSE was 34.58 μg/cm2, and the MAE was 26.27 μg/cm2. The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R2 values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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18 pages, 11917 KiB  
Article
Missing Plant Detection in Vineyards Using UAV Angled RGB Imagery Acquired in Dormant Period
by Salvatore Filippo Di Gennaro, Gian Luca Vannini, Andrea Berton, Riccardo Dainelli, Piero Toscano and Alessandro Matese
Drones 2023, 7(6), 349; https://doi.org/10.3390/drones7060349 - 26 May 2023
Cited by 2 | Viewed by 1957
Abstract
Since 2010, more and more farmers have been using remote sensing data from unmanned aerial vehicles, which have a high spatial–temporal resolution, to determine the status of their crops and how their fields change. Imaging sensors, such as multispectral and RGB cameras, are [...] Read more.
Since 2010, more and more farmers have been using remote sensing data from unmanned aerial vehicles, which have a high spatial–temporal resolution, to determine the status of their crops and how their fields change. Imaging sensors, such as multispectral and RGB cameras, are the most widely used tool in vineyards to characterize the vegetative development of the canopy and detect the presence of missing vines along the rows. In this study, the authors propose different approaches to identify and locate each vine within a commercial vineyard using angled RGB images acquired during winter in the dormant period (without canopy leaves), thus minimizing any disturbance to the agronomic practices commonly conducted in the vegetative period. Using a combination of photogrammetric techniques and spatial analysis tools, a workflow was developed to extract each post and vine trunk from a dense point cloud and then assess the number and position of missing vines with high precision. In order to correctly identify the vines and missing vines, the performance of four methods was evaluated, and the best performing one achieved 95.10% precision and 92.72% overall accuracy. The results confirm that the methodology developed represents an effective support in the decision-making processes for the correct management of missing vines, which is essential for preserving a vineyard’s productive capacity and, more importantly, to ensure the farmer’s economic return. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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23 pages, 13722 KiB  
Article
Numerical Analysis and Wind Tunnel Validation of Droplet Distribution in the Wake of an Unmanned Aerial Spraying System in Forward Flight
by Manuel Carreño Ruiz, Nicoletta Bloise, Giorgio Guglieri and Domenic D’Ambrosio
Drones 2022, 6(11), 329; https://doi.org/10.3390/drones6110329 - 29 Oct 2022
Cited by 9 | Viewed by 1911
Abstract
Recent developments in agriculture mechanization have generated significant challenges towards sustainable approaches to reduce the environmental footprint and improve food quality. This paper highlights the benefits of using unmanned aerial systems (UASs) for precision spraying applications of pesticides, reducing the environmental risk and [...] Read more.
Recent developments in agriculture mechanization have generated significant challenges towards sustainable approaches to reduce the environmental footprint and improve food quality. This paper highlights the benefits of using unmanned aerial systems (UASs) for precision spraying applications of pesticides, reducing the environmental risk and waste caused by spray drift. Several unmanned aerial spraying system (UASS) operation parameters and spray system designs are examined to define adequate configurations for specific treatments. A hexarotor DJI Matrice 600 equipped with T-Motor “15 × 5” carbon fiber blades is tested numerically using computational fluid dynamics (CFD) and experimentally in a wind tunnel. These tests assess the aerodynamic interaction between the wake of an advancing multicopter and the fine droplets generated by atomizers traditionally used in agricultural applications. The aim of this research is twofold. First, we analyze the effects of parameters such as flight speed (0, 2, and 3 m·s−1), nozzle type (hollowcone and fan), and injection pressure (2–3 bar) on spray distribution. In the second phase, we use data from the experimental campaign to validate numerical tools for the simulation of rotor–droplet interactions necessary to predict spray’s ground footprint and to plan a precise guidance algorithm to achieve on-target deposition and reduce the well-known droplet drift problem. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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Review

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29 pages, 5721 KiB  
Review
An Intelligent Grazing Development Strategy for Unmanned Animal Husbandry in China
by Yuanyang Cao, Tao Chen, Zichao Zhang and Jian Chen
Drones 2023, 7(9), 542; https://doi.org/10.3390/drones7090542 - 22 Aug 2023
Cited by 1 | Viewed by 1521
Abstract
Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has [...] Read more.
Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has been seriously damaged. The implementation of grazing prohibition, rotational grazing and the development of a large-scale breeding industry have not only ensured the supply of animal husbandry products, but also promoted the restoration of the grassland ecosystem. For the large-scale breeding industry, the animal welfare of livestock cannot be guaranteed due to the narrow and crowded space, thus, the production of the breeding industry usually has lower competitiveness than grazing. Disorderly grazing leads to grassland ecological crises; however, intelligent grazing can not only ensure animal welfare, but also fully improve the competitiveness of livestock husbandry products. Under the development of urbanization, the workforce engaged in grazing and breeding in pastoral areas is gradually lost. Intelligent grazing breeding methods need to be developed and popularized. This paper focuses on intelligent grazing, reviews grass remote sensing and aerial seeding, wearable monitoring equipment of livestock, UAV monitoring and intelligent grazing robots, and summarizes the development of intelligent grazing elements, exploring the new development direction of automatic grazing management with the grazing robot at this stage. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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23 pages, 6249 KiB  
Review
Independent Control Spraying System for UAV-Based Precise Variable Sprayer: A Review
by Adhitya Saiful Hanif, Xiongzhe Han and Seung-Hwa Yu
Drones 2022, 6(12), 383; https://doi.org/10.3390/drones6120383 - 28 Nov 2022
Cited by 21 | Viewed by 10508
Abstract
Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial [...] Read more.
Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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