Mobile robotics in smart farming: current trends and applications

In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations

This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released.

A Comprehensive Review of Path Planning for Agricultural Ground Robots

The population of the world is predicted to reach nine billion by 2050, implying that agricultural output must continue to rise. To deal with population expansion, agricultural chores must be mechanized and automated. Over the last decade, ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. This research examines several agricultural applications as well as the path planning approach used. The purpose of this study is to investigate the current literature on path/trajectory planning aspects of ground robots in agriculture using a systematic literature review technique, to contribute to the goal of contributing new information in the field. Coverage route planning appears to be less advanced in agriculture than point-to-point path routing, according to the finding, which is due to the fact that covering activities are usually required for agricultural applications, but precision agriculture necessitates point-to-point navigation. In the recent era, precision agriculture is getting more attention. The conclusion presented here demonstrates that both field coverage and point-to-point navigation have been applied successfully in path planning for agricultural robots.

Improving Sustainable Vegetation Indices Processing on Low-Cost Architectures

The development of embedded systems in sustainable precision agriculture has provided an important benefit in terms of processing time and accuracy of results, which has influenced the revolution in this field of research. This paper presents a study on vegetation monitoring algorithms based on Normalized Green-Red Difference Index (NGRDI) and Visible Atmospherically Resistant Index (VARI) in agricultural areas using embedded systems. These algorithms include processing and pre-processing to increase the accuracy of sustainability monitoring. The proposed algorithm was evaluated on a real database in the Souss Massa region in Morocco. The collection of data was based on unmanned aerial vehicles images hand data using four different agricultural products. The results in terms of processing time have been implemented on several architectures: Desktop, Odroid XU4, Jetson Nano, and Raspberry. However, this paper introduces a thorough study of the Hardware/Software Co-Design approach to choose the most suitable system for our proposed algorithm that responds to the different temporal and architectural constraints. The evaluation proved that we could process 311 frames/s in the case of low resolution, which gives real-time processing for agricultural field monitoring applications. The evaluation of the proposed algorithm on several architectures has shown that the low-cost XU4 card gives the best results in terms of processing time, power consumption, and computation flexibility.

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

SORA Methodology for Multi-UAS Airframe Inspections in an Airport

Deploying Unmanned Aircraft Systems (UAS) in safety- and business-critical operations requires demonstrating compliance with applicable regulations and a comprehensive understanding of the residual risk associated with the UAS operation. To support these activities and enable the safe deployment of UAS into civil airspace, the European Union Aviation Safety Agency (EASA) has established a UAS regulatory framework that mandates the execution of safety risk assessment for UAS operations in order to gain authorization to carry out certain types of operations. Driven by this framework, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released the Specific Operation Risk Assessment (SORA) methodology that guides the systematic risk assessment for UAS operations. However, existing work on SORA and its applications focuses mainly on single UAS operations, offering limited support for assuring operations conducted with multiple UAS and with autonomous features. Therefore, the work presented in this paper analyzes the application of SORA for a Multi-UAS airframe inspection (AFI) operation, that involves deploying multiple UAS with autonomous features inside an airport. We present the decision-making process of each SORA step and its application to a multiple UAS scenario. The results shows that the procedures and safety features included in the Multi-AFI operation such as workspace segmentation, the independent multi-UAS AFI crew proposed, and the mitigation actions provide confidence that the operation can be conducted safely and can receive a positive evaluation from the competent authorities. We also present our key findings from the application of SORA and discuss how it can be extended to better support multi-UAS operations.

3D Reconstruction of Non-Rigid Plants and Sensor Data Fusion for Agriculture Phenotyping

Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop’s life cycle monitoring. This work presents, which we believe to be the first time, a system capable of generating 3D models of non-rigid corn plants, which can be used as a tool in the phenotyping process. The system is composed by two modules: an terrestrial acquisition module and a processing module. The terrestrial acquisition module is composed by a robot, equipped with an RGB-D camera and three sets of temperature, humidity, and luminosity sensors, that collects data in the field. The processing module conducts the non-rigid 3D plants reconstruction and merges the sensor data into these models. The work presented here also shows a novel technique for background removal in depth images, as well as efficient techniques for processing these images and the sensor data. Experiments have shown that from the models generated and the data collected, plant structural measurements can be performed accurately and the plant’s environment can be mapped, allowing the plant’s health to be evaluated and providing greater crop efficiency.

3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.