Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming

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Topic Information

Dear Colleagues,

New projections of escalating population growth around the planet come as the world faces major global challenges such as climate change, environmental degradation, and food insecurity. Digital farming based on sustainable agricultural practices will potentially overcome the above challenges, simultaneously increasing crop yield while reducing farming inputs.

Over the last decade, unmanned ground and aerial vehicles (UGVs-UAVs) have become a significant tool for digital farming, providing real-time precisely located and scalable data.

This Topic aims to keep up with progress on the latest applications derived from the use of unmanned ground and aerial vehicles (UGVs-UAVs) within the digital farming framework, including the evolution of integrating precision crop management systems and smart operations, as well as pointing out the challenges still ahead.

At the same time, this Topic also relies on the CHAMELEON Project (https://chameleon-heu.eu/about/). The CHAMELEON Project (flyer can be downloaded) activities have set ambitious targets to address a common challenge among EU countries: to support key areas such as agriculture, forestry, livestock, and rural development towards their conversion to sustainable and digital sectors, through the development of an integrated network of collaborating agents, equipped with advanced sensing and cognitive capabilities that can support multiple missions at tactical level. As a result, CHAMELEON will develop a Drone Innovation Platform and support the digital transformation of the agriculture, forestry, and livestock sectors in Europe.

Novel improvements in both methodologies with advanced data and analytic algorithms and techniques that accomplish management operations in an autonomous way from drones are welcome.

Dr. Monica Herrero-Huerta
Dr. Jose A. Jiménez-Berni
Dr. Shangpeng Sun
Dr. Ittai Herrmann
Prof. Dr. Diego González-Aguilera
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	19.2 Days	CHF 2600
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

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Published Papers (16 papers)

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17 pages, 6037 KiB  

Open AccessArticle

Evaluation of Mosaic Image Quality and Analysis of Influencing Factors Based on UAVs

by Xiaoyue Du, Liyuan Zheng, Jiangpeng Zhu, Haiyan Cen and Yong He

Drones 2024, 8(4), 143; https://doi.org/10.3390/drones8040143 - 4 Apr 2024

Cited by 1 | Viewed by 2271

Abstract

With the growing prominence of UAV-based low-altitude remote sensing in agriculture, the acquisition and processing of high-quality UAV remote sensing images is paramount. The purpose of this study is to investigate the impact of various parameter settings on image quality and optimize these [...] Read more.

With the growing prominence of UAV-based low-altitude remote sensing in agriculture, the acquisition and processing of high-quality UAV remote sensing images is paramount. The purpose of this study is to investigate the impact of various parameter settings on image quality and optimize these parameters for UAV operations to enhance efficiency and image quality. The study examined the effects of three parameter settings (exposure time, flight altitudes and forward overlap (OF)) on image quality and assessed images obtained under various conditions using signal-to-noise ratio (SNR) and BRISQUE algorithms. The results indicate that the setting of exposure time during UAV image acquisition directly affects image quality, with shorter exposure times resulting in lower SNR. The optimal exposure times for the RGB and MS cameras have been determined as 0.8 ms to 1.1 ms and 4 ms to 16 ms, respectively. Additionally, the best image quality is observed at flight altitudes between 15 and 35 m. The setting of UAV OF complements exposure time and flight altitude; to ensure the completeness of image acquisition, it is suggested that the flight OF is set to approximately 75% at a flight altitude of 25 m. Finally, the proposed image redundancy removal method has been demonstrated as a feasible approach for reducing image mosaicking time (by 84%) and enhancing the quality of stitched images (by 14%). This research has the potential to reduce flight costs, improve image quality, and significantly enhance agricultural production efficiency. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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21 pages, 7646 KiB  

Open AccessArticle

Research on Path Tracking of Articulated Steering Tractor Based on Modified Model Predictive Control

by Baocheng Zhou, Xin Su, Hongjun Yu, Wentian Guo and Qing Zhang

Agriculture 2023, 13(4), 871; https://doi.org/10.3390/agriculture13040871 - 15 Apr 2023

Cited by 17 | Viewed by 3346

Abstract

With the development of agricultural mechanization and information technology, automatic navigation tractors are becoming a more common piece of farm equipment. The accuracy of automatic navigation tractor path tracking has become critical for maximizing efficiency and crop yield. Aiming at improving path tracking [...] Read more.

With the development of agricultural mechanization and information technology, automatic navigation tractors are becoming a more common piece of farm equipment. The accuracy of automatic navigation tractor path tracking has become critical for maximizing efficiency and crop yield. Aiming at improving path tracking control accuracy and the real-time performance of the traditional model predictive control (MPC) algorithm, the study proposed an adaptive time-domain parameter with MPC in the path tracking control of the articulated steering tractor. Firstly, the kinematics model of the articulated steering tractor was established, as well as the multi-body dynamics model by RecurDyn. Secondly, the genetic algorithm was combined with MPC. The genetic algorithm was used to calculate the optimal time domain parameters under real-time vehicle speed, vehicle posture and road conditions, and the adaptive MPC was realized. Then, path tracking simulations were conducted by combining RecurDyn and Simulink under different path types. Compared with the traditional MPC algorithm under the three paths of U-shaped, figure-eight-shaped and complex curves, the maximum lateral deviations of the modified MPC algorithm were reduced by 59.0%, 24.9% and 13.2%, respectively. At the same time, the average lateral deviation was reduced by 72%, 43.5% and 20.3%, respectively. Finally, the real path tracking tests of the articulated steering tractor were performed. The test results indicated that under the three path tracking conditions of straight line, front wheel steering and articulated steering, the maximum lateral deviation of the modified MPC algorithm was reduced by 67.8%, 44.7% and 45.1% compared with the traditional MPC. The simulation analysis and real tractor tests verified the proposed MPC algorithm, considering the adaptive time-domain parameter has a smaller deviation and can quickly eliminate the deviation and maintain tracking stability. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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18 pages, 5196 KiB  

Open AccessArticle

Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters

by Wei Luo, Yongxiang Zhao, Quanqin Shao, Xiaoliang Li, Dongliang Wang, Tongzuo Zhang, Fei Liu, Longfang Duan, Yuejun He, Yancang Wang, Guoqing Zhang, Xinghui Wang and Zhongde Yu

Sensors 2023, 23(8), 3948; https://doi.org/10.3390/s23083948 - 13 Apr 2023

Cited by 8 | Viewed by 2392

Abstract

This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm [...] Read more.

This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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16 pages, 2440 KiB  

Open AccessArticle

Centralized Mission Planning for Multiple Robots Minimizing Total Mission Completion Time

by Nam Eung Hwang, Hyung Jun Kim and Jae Gwan Kim

Appl. Sci. 2023, 13(6), 3737; https://doi.org/10.3390/app13063737 - 15 Mar 2023

Cited by 2 | Viewed by 1754

Abstract

Most mission planning algorithms solve multi-robot-multi-mission problems based on mixed integer linear programming. In these algorithms, the rewards (or costs) of missions for each robot are calculated according to the purpose of the user. Then, the (robot-mission) pair that has maximum rewards (or [...] Read more.

Most mission planning algorithms solve multi-robot-multi-mission problems based on mixed integer linear programming. In these algorithms, the rewards (or costs) of missions for each robot are calculated according to the purpose of the user. Then, the (robot-mission) pair that has maximum rewards (or minimum costs) is found in the rewards (or costs) table and the mission is allocated to the robot. However, it is hard to design the reward for minimizing total mission completion time because not only a robot, but also the whole robots’ mission plans must be considered to achieve the purpose. In this paper, we propose centralized mission planning for multi-robot-multi-mission problems, minimizing total mission completion time. First, mission planning for single-robot-multi-mission problems is proposed because it is easy to solve. Then, this method is applied for multi-robot-multi-mission problems, adding a mission-plan-adjustment step. To show the excellent performance of the suggested algorithm in diverse situations, we demonstrate simulations for 3 representative cases: a simple case, which is composed of 3 robots and 8 missions, a medium case, which is composed of 4 robots and 30 missions, and a huge case, which is composed of 6 robots and 50 missions. The total mission completion time of the proposed algorithm for each case is lower than the results of the existing algorithm. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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16 pages, 8544 KiB  

Open AccessArticle

Structural Component Phenotypic Traits from Individual Maize Skeletonization by UAS-Based Structure-from-Motion Photogrammetry

by Monica Herrero-Huerta, Diego Gonzalez-Aguilera and Yang Yang

Drones 2023, 7(2), 108; https://doi.org/10.3390/drones7020108 - 4 Feb 2023

Cited by 9 | Viewed by 3270

Abstract

The bottleneck in plant breeding programs is to have cost-effective high-throughput phenotyping methodologies to efficiently describe the new lines and hybrids developed. In this paper, we propose a fully automatic approach to overcome not only the individual maize extraction but also the trait [...] Read more.

The bottleneck in plant breeding programs is to have cost-effective high-throughput phenotyping methodologies to efficiently describe the new lines and hybrids developed. In this paper, we propose a fully automatic approach to overcome not only the individual maize extraction but also the trait quantification challenge of structural components from unmanned aerial system (UAS) imagery. The experimental setup was carried out at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette (IN, USA). On 27 July and 3 August 2021, two flights were performed over maize trials using a custom-designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor onboard. RGB images were processed using a standard photogrammetric pipeline based on structure from motion (SfM) to obtain a final scaled 3D point cloud of the study field. Individual plants were extracted by, first, semantically segmenting the point cloud into ground and maize using 3D deep learning. Secondly, we employed a connected component algorithm to the maize end-members. Finally, once individual plants were accurately extracted, we robustly applied a Laplacian-based contraction skeleton algorithm to compute several structural component traits from each plant. The results from phenotypic traits such as height and number of leaves show a determination coefficient (R²) with on-field and digital measurements, respectively, better than 90%. Our test trial reveals the viability of extracting several phenotypic traits of individual maize using a skeletonization approach on the basis of a UAS imagery-based point cloud. As a limitation of the methodology proposed, we highlight that the lack of plant occlusions in the UAS images obtains a more complete point cloud of the plant, giving more accuracy in the extracted traits. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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37 pages, 9365 KiB  

Open AccessArticle

Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud

by Wenli Zhang, Xinyu Peng, Guoqiang Cui, Haozhou Wang, Daisuke Takata and Wei Guo

Drones 2023, 7(2), 65; https://doi.org/10.3390/drones7020065 - 17 Jan 2023

Cited by 6 | Viewed by 5075

Abstract

Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the [...] Read more.

Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV RGB images with photogrammetry. We conducted a comparison experiment by flying a UAV from two altitudes (50 m and 20 m) above a university orchard with several fruit tree species and developed three metrics, namely the F1-score of bifurcation point (FBP), the F1-score of end point (FEP), and the Hausdorff distance (HD) to evaluate the performance of the proposed algorithm. The results show that the average values of FBP, FEP, and HD for the point cloud of fruit tree branches collected at 50 m altitude were 64.15%, 69.94%, and 0.0699, respectively, and those at 20 m were 83.24%, 84.66%, and 0.0474, respectively. This paper provides a branch skeleton extraction method for low-cost 3D digital management of orchards, which can effectively extract the main skeleton from the sparse fruit tree branch point cloud, can assist in analyzing the growth state of different types of fruit trees, and has certain practical application value in the management of orchards. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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45 pages, 6835 KiB  

Open AccessReview

UAV Formation Trajectory Planning Algorithms: A Review

by Yunhong Yang, Xingzhong Xiong and Yuehao Yan

Drones 2023, 7(1), 62; https://doi.org/10.3390/drones7010062 - 16 Jan 2023

Cited by 76 | Viewed by 26074

Abstract

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. [...] Read more.

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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13 pages, 3800 KiB  

Open AccessArticle

Smart Decision-Support System for Pig Farming

by Hao Wang, Boyang Li, Haoming Zhong, Ahong Xu, Yingjie Huang, Jingfu Zou, Yuanyuan Chen, Pengcheng Wu, Yiqiang Chen, Cyril Leung and Chunyan Miao

Drones 2022, 6(12), 389; https://doi.org/10.3390/drones6120389 - 30 Nov 2022

Cited by 4 | Viewed by 3460

Abstract

There are multiple participants, such as farmers, wholesalers, retailers, financial institutions, etc., involved in the modern food production process. All of these participants and stakeholders have a shared goal, which is to gather information on the food production process so that they can [...] Read more.

There are multiple participants, such as farmers, wholesalers, retailers, financial institutions, etc., involved in the modern food production process. All of these participants and stakeholders have a shared goal, which is to gather information on the food production process so that they can make appropriate decisions to increase productivity and reduce risks. However, real-time data collection and analysis continue to be difficult tasks, particularly in developing nations, where agriculture is the primary source of income for the majority of the population. In this paper, we present a smart decision-support system for pig farming. Specifically, we first adopt rail-based unmanned vehicles to capture pigsty images. We then conduct image stitching to avoid double-counting pigs so that we can use image segmentation method to give precise masks for each pig. Based on the segmentation masks, the pig weights can be estimated, and data can be integrated in our developed mobile app. The proposed system enables the above participants and stakeholders to have real-time data and intelligent analysis reports to help their decision-making. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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14 pages, 2090 KiB  

Open AccessArticle

Auto-Encoder Learning-Based UAV Communications for Livestock Management

by Mohammed A. Alanezi, Abdullahi Mohammad, Yusuf A. Sha’aban, Houssem R. E. H. Bouchekara and Mohammad S. Shahriar

Drones 2022, 6(10), 276; https://doi.org/10.3390/drones6100276 - 25 Sep 2022

Cited by 8 | Viewed by 3781

Abstract

The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system [...] Read more.

The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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25 pages, 33522 KiB  

Open AccessArticle

Automatic Detection of Olive Tree Canopies for Groves with Thick Plant Cover on the Ground

by Sergio Illana Rico, Diego Manuel Martínez Gila, Pablo Cano Marchal and Juan Gómez Ortega

Sensors 2022, 22(16), 6219; https://doi.org/10.3390/s22166219 - 19 Aug 2022

Cited by 9 | Viewed by 2869

Abstract

Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to [...] Read more.

Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the ground truth obtained by measurements made with other types of sensors. Marking these canopies manually is an arduous and tedious process that is replaced by automatic methods that rarely work well for groves with a thick plant cover on the ground. This paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera, regardless of the plant cover density between canopies. The method is based on the relative spatial information between canopies.The planting pattern used by the grower is computed and extrapolated using Delaunay triangulation in order to fuse this knowledge with that previously obtained from spectral information. It is shown that the minimisation of a certain function provides an optimal fit of the parameters that define the marking of the trees, yielding promising results of 77.5% recall and 70.9% precision. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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20 pages, 3195 KiB  

Open AccessArticle

UBER: UAV-Based Energy-Efficient Reconfigurable Routing Scheme for Smart Wireless Livestock Sensor Network

by Mohammed A. Alanezi, Abdulazeez F. Salami, Yusuf A. Sha’aban, Houssem R. E. H. Bouchekara, Mohammad S. Shahriar, Mohammed Khodja and Mostafa K. Smail

Sensors 2022, 22(16), 6158; https://doi.org/10.3390/s22166158 - 17 Aug 2022

Cited by 15 | Viewed by 2953

Abstract

This paper addresses coverage loss and rapid energy depletion issues for wireless livestock sensor networks by proposing a UAV-based energy-efficient reconfigurable routing (UBER) scheme for smart wireless livestock sensor networking applications. This routing scheme relies on a dynamic residual energy thresholding strategy, robust [...] Read more.

This paper addresses coverage loss and rapid energy depletion issues for wireless livestock sensor networks by proposing a UAV-based energy-efficient reconfigurable routing (UBER) scheme for smart wireless livestock sensor networking applications. This routing scheme relies on a dynamic residual energy thresholding strategy, robust cluster-to-UAV link formation, and UAV-assisted network coverage and recovery mechanism. The performance of UBER was evaluated using low, normal and high UAV altitude scenarios. Performance metrics employed for this analysis are network stability (NST), load balancing ratio (LBR), and topology fluctuation effect ratio (TFER). Obtained results demonstrated that operating with a UAV altitude of 230 m yields gains of 31.58%, 61.67%, and 75.57% for NST, LBR, and TFER, respectively. A comparative performance evaluation of UBER was carried out with respect to hybrid heterogeneous routing (HYBRID) and mobile sink using directional virtual coordinate routing (MS-DVCR). The performance indicators employed for this comparative analysis are energy consumption (ENC), network coverage (COV), received packets (RPK), SN failures detected (SNFD), route failures detected (RFD), routing overhead (ROH), and end-to-end delay (ETE). With regard to the best-obtained results, UBER recorded performance gains of 46.48%, 47.33%, 15.68%, 19.78%, 46.44%, 29.38%, and 58.56% over HYBRID and MS-DVCR in terms of ENC, COV, RPK, SNFD, RFD, ROH, and ETE, respectively. The results obtained demonstrated that the UBER scheme is highly efficient with competitive performance against the benchmarked CBR schemes. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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14 pages, 4035 KiB  

Open AccessArticle

Adaptive Sliding Mode Path Tracking Control of Unmanned Rice Transplanter

by Jinyang Li, Zhijian Shang, Runfeng Li and Bingbo Cui

Agriculture 2022, 12(8), 1225; https://doi.org/10.3390/agriculture12081225 - 15 Aug 2022

Cited by 17 | Viewed by 3223

Abstract

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control [...] Read more.

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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17 pages, 46199 KiB  

Open AccessArticle

Vision-Based Module for Herding with a Sheepdog Robot

by Virginia Riego del Castillo, Lidia Sánchez-González, Adrián Campazas-Vega and Nicola Strisciuglio

Sensors 2022, 22(14), 5321; https://doi.org/10.3390/s22145321 - 16 Jul 2022

Cited by 10 | Viewed by 3694

Abstract

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian [...] Read more.

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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21 pages, 92027 KiB  

Open AccessArticle

Similarity Analysis between Contour Lines by Remotely Piloted Aircraft and Topography Using Hausdorff Distance: Application on Contour Planting

by Alexandre Araujo Ribeiro Freire, Mauro Antonio Homem Antunes, Murilo Machado de Barros, Wagner Dias de Souza, Wesley de Sousa da Silva and Thaís Machado de Souza

Remote Sens. 2022, 14(14), 3269; https://doi.org/10.3390/rs14143269 - 7 Jul 2022

Cited by 3 | Viewed by 2497

Abstract

Contour planting minimizes soil degradation, making agricultural production more sustainable. Currently, geotechnologies can provide more precise and fast data from relief than rudimentary data acquisition for agricultural management. Thus, the objective of this work was to analyze the similarities between contour lines from [...] Read more.

Contour planting minimizes soil degradation, making agricultural production more sustainable. Currently, geotechnologies can provide more precise and fast data from relief than rudimentary data acquisition for agricultural management. Thus, the objective of this work was to analyze the similarities between contour lines from topography and Remotely Piloted Aircraft, using the Hausdorff distance algorithm. This study was carried out in the period between January 2020 and November 2021 in four localities in the State of Rio de Janeiro, Brazil: two areas located in the municipality of Bom Jardim and two areas in the municipality of Seropédica. Data were acquired through a conventional topographic survey and an aerial photogrammetric survey by Remotely Piloted Aircraft. From the acquired field data for the studied areas, the Digital Elevation Models were generated with a spatial resolution of 0.20 m and the contour lines with an equidistance of one meter. The contour lines obtained by both techniques were superimposed and their similarity was verified using the Hausdorff distance. The results show that there was a better similarity among the contour lines in areas with a very rugged relief than in a smooth relief. Also, the lowest altimetric differences observed in the Digital Elevation Models were associated with the smallest Hausdorff distance. These adjustments correspond, respectively, to the segments between the contour lines with the best and the worst individual similarity for each area. We observed that the similarity between the contour lines from topography and RPA yielded slope differences lower than 6.1% for at least 95% of all studied areas. The Hausdorff distance analysis allowed us to conclude that contour planting can be performed from data obtained via Remotely Piloted Aircraft, provided that vertical accuracy analysis controls the quality of the Digital Elevation Models. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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16 pages, 6168 KiB  

Open AccessArticle

Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images

by Xin Yang, Shichen Gao, Qian Sun, Xiaohe Gu, Tianen Chen, Jingping Zhou and Yuchun Pan

Agriculture 2022, 12(7), 970; https://doi.org/10.3390/agriculture12070970 - 6 Jul 2022

Cited by 12 | Viewed by 3181

Abstract

Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories [...] Read more.

Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RGB and multispectral images were analyzed. The VGG-16, Inception-V3 and ResNet-50 algorithms were trained and compared depending on classification accuracy and Kappa coefficient. The results showed that the more severe the lodging, the higher the intensity value and spectral reflectance of RGB and multispectral image. The reflectance variation in red edge band were more evident than that in visible band with different lodging extents. The classification performance using multispectral images was better than that of RGB images in various lodging extents. The test accuracies of three deep learning algorithms in non-lodging based on RGB images were high, i.e., over 90%, but the classification performance between moderate lodging and severe lodging needed to be improved. The test accuracy of ResNet-50 was 96.32% with Kappa coefficients of 0.9551 by using multispectral images, which was superior to VGG-16 and Inception-V3, and the accuracies of ResNet-50 on each lodging subdivision category all reached 96%. The ResNet-50 algorithm of deep learning combined with multispectral images can realize accurate lodging classification to promote post-stress field management and production assessment. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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23 pages, 4734 KiB  

Open AccessArticle

Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules

by Nam Eung Hwang, Hyung Jun Kim and Jae Gwan Kim

Appl. Sci. 2022, 12(13), 6780; https://doi.org/10.3390/app12136780 - 4 Jul 2022

Cited by 7 | Viewed by 2481

Abstract

In this paper, we propose a centralized task allocation and an alignment technique based on constraint table and alignment rules. For task allocation, a scoring scheme has to be set. The existing time-discounted scoring scheme has two problems; if the score is calculated [...] Read more.

In this paper, we propose a centralized task allocation and an alignment technique based on constraint table and alignment rules. For task allocation, a scoring scheme has to be set. The existing time-discounted scoring scheme has two problems; if the score is calculated based on arrival time, the agent who arrives in a task point first may finish the task late, and if the score is calculated based on end-time of the task, agents who have the same score may appear because of temporal constraints. Therefore, a modified time-discounted reward scheme based on both arrival and end-time is proposed. Additionally, an accumulated distance cost scheme is proposed for minimum fuel consumption. The constraint table made by tasks that are already aligned is also considered in scoring. For centralized task alignment based on the constraint table and alignment rules, a technique based on sequential greedy algorithm is proposed. Resolving conflicts on alignment is described in detail using constraint table and alignment rules, which are composed of four basic principles. We demonstrate simulations about task allocation and alignment for multi-agent with coupled constraints. Simple and complicated cases are used to verify the scoring schemes and the proposed techniques. Additionally, a huge case is used to show computational efficiency. The results are feasibly good when the constraints are properly set. Full article

(This article belongs to the Topic Unmanned Ground and Aerial Vehicles (UGVs-UAVs) for Digital Farming)

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