A multi-modality ground-to-air cross-view pose estimation dataset for field robots

High-precision localization is critical for intelligent robotics in autonomous driving, smart agriculture, and military operations. While Global Navigation Satellite System (GNSS) provides global positioning, its reliability deteriorates severely in signal degraded environments like urban canyons. Cross-view pose estimation using aerial-ground sensor fusion offers an economical alternative, yet current datasets lack field scenarios and high-resolution LiDAR support.This work introduces a multimodal cross-view dataset addressing these gaps. It contains 29,940 synchronized frames across 11 operational environments (6 field environments, 5 urban roads), featuring: 1) 144-channel LiDAR point clouds, 2) ground-view RGB images, and 3) aerial orthophotos. Centimeter-accurate georeferencing is ensured through GNSS fusion and post-processed kinematic positioning. The dataset uniquely integrates field environments and high-resolution LiDAR-aerial-ground data triplets, enabling rigorous evaluation of 3-DoF pose estimation algorithms for orientation alignment and coordinate transformation between perspectives.This resource supports development of robust localization systems for field robots in GNSS-denied conditions, emphasizing cross-view feature matching and multisensor fusion. Light Detection And Ranging (LiDAR)-enhanced ground truth further distinguishes its utility for complex outdoor navigation research.

Industrial Robotics and the Future of Work

Starting in the 1970s with robots that were physically isolated from contact with their human co-workers, robots now collaborate with human workers towards a common task goal in a shared workspace. This type of robotic device represents a new era of workplace automation. Industrial robotics is rapidly evolving due to advances in sensor technology, artificial intelligence (AI), wireless communications, mechanical engineering, and materials science. While these new robotic devices are used mainly in manufacturing and warehousing, human-robot collaboration is now seen across multiple goods-producing and service-delivery industry sectors. Assessing and controlling the risks of human-robot collaboration is a critical challenge for occupational safety and health research and practice as industrial robotics becomes a pervasive feature of the future of work. Understanding the physical, psychosocial, work organization, and cybersecurity risks associated with the increasing use of robotic technologies is critical to ensuring the safe development and implementation of industrial robotics. This commentary provides a brief review of the uses of robotic technologies across selected industry sectors; the risks of current and future industrial robotic applications for worker and employer alike; strategies for integrating human-robot collaboration into a health and safety management system; and the role of robotic safety standards in the future of work.

Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators

Agriculture needs to produce more with fewer resources to satisfy the world's demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier. This research addresses the challenges posed by low-cost manipulators, such as inaccuracy, limited sensor feedback, and dynamic uncertainties. Three control strategies for a low-cost agricultural SCARA manipulator were developed and benchmarked: a Sliding Mode Controller (SMC), a Reinforcement Learning (RL) Controller, and a novel Proportional-Integral (PI) controller with a self-tuning feedforward element (PIFF). The results show the best response time was obtained using the SMC, but with joint movement jitter. The RL controller showed sudden breaks and overshot upon reaching the setpoint. Finally, the PIFF controller showed the smoothest reference tracking but was more susceptible to changes in system dynamics.

RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention

The widespread cultivation of apples highlights the importance of efficient and accurate apple detection algorithms in robotic picking technology. The accuracy of current apple picking detection algorithms is still limited when the distribution is dense and occlusion exists, and there is a significant challenge in deploying current high accuracy detection models on edge devices with limited computational resources. To solve the above problems, this paper proposes an improved detection algorithm (RE-YOLO) based on YOLOv8n. First, this paper innovatively introduces Receptive-Field Attention Convolution (RFAConv) to improve the backbone and neck network of YOLOv8. It essentially solves the problem of convolution kernel parameter sharing and improves the consideration of the differential information from different locations, which significantly improves the accuracy of model recognition. Second, this paper innovatively proposes an EMA_C2f module. This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. The experimental results show that RE-YOLO improves the precision, recall, mAP@0.5, and mAP@0.5-0.95 by 2%, 2.1%, 2.7%, and 3.9%, respectively, compared with the original YOLOv8. Compared with YOLOv5, it improves 4%, 1.9%, 1.7% and 3%, respectively, which fully proves the advanced and practical nature of the proposed algorithm.

Translational nanorobotics breaking through biological membranes

In the dynamic realm of translational nanorobotics, the endeavor to develop nanorobots carrying therapeutics in rational in vivo applications necessitates a profound understanding of the biological landscape of the human body and its complexity. Within this landscape, biological membranes stand as critical barriers to the successful delivery of therapeutic cargo to the target site. Their crossing is not only a challenge for nanorobotics but also a pivotal criterion for the clinical success of therapeutic-carrying nanorobots. Nevertheless, despite their urgency, strategies for membrane crossing in translational nanorobotics remain relatively underrepresented in the scientific literature, signaling an opportunity for further research and innovation. This review focuses on nanorobots with various propulsion mechanisms from chemical and physical to hybrid mechanisms, and it identifies and describes four essential biological membranes that represent the barriers needed to be crossed in the therapeutic journey of nanorobots in in vivo applications. First is the entry point into the blood stream, which is the skin or mucosa or intravenous injection; next is the exit from the bloodstream across the endothelium to the target site; further is the entry to the cell through the plasma membrane and, finally, the escape from the lysosome, which otherwise destroys the cargo. The review also discusses design challenges inherent in translating nanorobot technologies to real-world applications and provides a critical overview of documented membrane crossings. The aim is to underscore the need for further interdisciplinary collaborations between chemists, materials scientists and chemical biologists in this vital domain of translational nanorobotics that has the potential to revolutionize the field of precision medicine.

Internet of things enabled smart agriculture: Current status, latest advancements, challenges and countermeasures

It is no wonder that agriculture plays a vital role in the development of some countries when their economies rely on agricultural activities and the production of food for human survival. Owing to the ever-increasing world population, estimated at 7.9 billion in 2022, feeding this number of people has become a concern due to the current rate of agricultural food production subjected to various reasons. The advent of the Internet of Things (IoT) based technologies in the 21st century has led to the reshaping of every industry, including agriculture, and has paved the way for smart agriculture, with the technology used towards automating and controlling most aspects of traditional agriculture. Smart agriculture, interchangeably known as smart farming, utilizes IoT and related enabling technologies such as cloud computing, artificial intelligence, and big data in agriculture and offers the potential to enhance agricultural operations by automating and making intelligent decisions, resulting in increased efficiency and a better yield with minimum waste. Consequently, most governments are spending more money and offering incentives to switch from traditional to smart agriculture. Nonetheless, the COVID-19 global pandemic served as a catalyst for change in the agriculture industry, driving a shift toward greater reliance on technology over traditional labor for agricultural tasks. In this regard, this research aims to synthesize the current knowledge of smart agriculture, highlighting its current status, main components, latest application areas, advanced agricultural practices, hardware and software used, success stores, potential challenges, and countermeasures to them, and future trends, for the growth of the industry as well as a reference to future research.

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task

OBJECTIVE

The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots.

BACKGROUND

Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated.

METHODS

Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics.

RESULTS

HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7).

CONCLUSION

HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation.

APPLICATION

These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Gripping Success Metric for Robotic Fruit Harvesting

Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the model selection process for object detection, the average precision (AP) score typically provides the de facto standard. However, AP is not intuitive for determining which model is most efficient for robotic harvesting. It is based on the intersection-over-union (IoU) of bounding boxes, which reflects only regional overlap. IoU alone cannot reliably predict the success of robotic gripping, as identical IoU scores may yield different results depending on the overlapping shape of the boxes. In this paper, we propose a novel evaluation metric for robotic harvesting. To assess gripping success, our metric uses the center coordinates of bounding boxes and a margin hyperparameter that accounts for the gripper's specifications. We conducted evaluation about popular object detection models on peach and apple datasets. The experimental results showed that the proposed gripping success metric is much more intuitive and helpful in interpreting the performance data.

Optimizing Peanut (Arachis hypogaea L.) Production: Genetic Insights, Climate Adaptation, and Efficient Management Practices: Systematic Review

Peanut production plays a crucial role in global food security, particularly in developing countries, where it provides essential nutrition and income. This paper examines the optimization of peanut production through genetic advancements, climate adaptation strategies, and sustainable practices. The primary objective is to increase yields by addressing challenges related to climate change, pests, and resource constraints. Globally, peanut production is hindered by rising temperatures, irregular rainfall, and declining soil quality, impacting both yield and quality. Developing countries, especially in Africa and Asia, face additional challenges, such as limited access to advanced agricultural technologies, inadequate infrastructure, and insufficient support for smallholder farmers. The vital issues include genetic vulnerabilities to pests, climate stress, and inefficient water use. Recent genetic research has provided insights into breeding more resilient, drought-resistant varieties, offering hope for improving yields, despite environmental challenges. The adoption of climate adaptation strategies, precision farming, and integrated pest management is essential for boosting productivity. These, along with optimized irrigation and nutrient management, have significantly impacted peanut production in resource-limited settings. Additionally, drought-resistant varieties have proven crucial, enabling farmers to increase resilience and yields in areas facing climate stress. In conclusion, optimizing peanut production requires continued investment in genetic advancements, infrastructure, and sustainable practices. Future efforts should focus on improving climate adaptation and sustainable farming techniques for long-term success.

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning

Widespread adaptation of autonomous, robotic systems relies greatly on safe and reliable operation, which in many cases is derived from the ability to maintain accurate and robust perception capabilities. Environmental and operational conditions as well as improper maintenance can produce calibration errors inhibiting sensor fusion and, consequently, degrading the perception performance and overall system usability. Traditionally, sensor calibration is performed in a controlled environment with one or more known targets. Such a procedure can only be carried out in between operations and is done manually; a tedious task if it must be conducted on a regular basis. This creates an acute need for online targetless methods, capable of yielding a set of geometric transformations based on perceived environmental features. However, the often-required redundancy in sensing modalities poses further challenges, as the features captured by each sensor and their distinctiveness may vary. We present a holistic approach to performing joint calibration of a camera-lidar-radar trio in a representative autonomous driving application. Leveraging prior knowledge and physical properties of these sensing modalities together with semantic information, we propose two targetless calibration methods within a cost minimization framework: the first via direct online optimization, and the second through self-supervised learning (SSL).

Human-Robot Interaction in Agriculture: A Systematic Review

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human-robot interactive systems.

Lab2Field transfer of a robotic raspberry harvester enabled by a soft sensorized physical twin

Robotic fruit harvesting requires dexterity to handle delicate crops and development relying upon field testing possible only during the harvesting season. Here we focus on raspberry crops, and explore how the research methodology of harvesting robots can be accelerated through soft robotic technologies. We propose and demonstrate a physical twin of the harvesting environment: a sensorized physical simulator of a raspberry plant with tunable properties, used to train a robotic harvester in the laboratory regardless of season. The sensors on the twin allow for direct comparison with human demonstrations, used to tune the robot controllers. In early field demonstrations, an 80% harvesting success rate was achieved without any modifications on the lab trained robot.

Kai Junge and colleagues designed a soft sensorized physical twin of a raspberry plant which they use to collect force data on fruit picking to train a robotic harvester. Early field demonstrations showed promise in rapid training of a robot for the delicate task of soft fruit picking.

Maneuvering on non-Newtonian fluidic terrain: a survey of animal and bio-inspired robot locomotion techniques on soft yielding grounds

Frictionally yielding media are a particular type of non-Newtonian fluids that significantly deform under stress and do not recover their original shape. For example, mud, snow, soil, leaf litters, or sand are such substrates because they flow when stress is applied but do not bounce back when released. Some robots have been designed to move on those substrates. However, compared to moving on solid ground, significantly fewer prototypes have been developed and only a few prototypes have been demonstrated outside of the research laboratory. This paper surveys the existing biology and robotics literature to analyze principles of physics facilitating motion on yielding substrates. We categorize animal and robot locomotion based on the mechanical principles and then further on the nature of the contact: discrete contact, continuous contact above the material, or through the medium. Then, we extract different hardware solutions and motion strategies enabling different robots and animals to progress. The result reveals which design principles are more widely used and which may represent research gaps for robotics. We also discuss that higher level of abstraction helps transferring the solutions to the robotics domain also when the robot is not explicitly meant to be bio-inspired. The contribution of this paper is a review of the biology and robotics literature for identifying locomotion principles that can be applied for future robot design in yielding environments, as well as a catalog of existing solutions either in nature or man-made, to enable locomotion on yielding grounds.

Internet of Underground Things in Agriculture 4.0: Challenges, Applications and Perspectives

Internet of underground things (IoUTs) and wireless underground sensor networks (WUSNs) are new technologies particularly relevant in agriculture to measure and transmit environmental data, enabling us to optimize both crop growth and water resource management. The sensor nodes can be buried anywhere, including in the passage of vehicles, without interfering with aboveground farming activities. However, to obtain fully operational systems, several scientific and technological challenges remain to be addressed. The objective of this paper is to identify these challenges and provide an overview of the latest advances in IoUTs and WUSNs. The challenges related to the development of buried sensor nodes are first presented. The recent approaches proposed in the literature to autonomously and optimally collect the data of several buried sensor nodes, ranging from the use of ground relays, mobile robots and unmanned aerial vehicles, are next described. Finally, potential agricultural applications and future research directions are identified and discussed.

A Survey of Robotic Harvesting Systems and Enabling Technologies

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.

Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study

The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, and efficient operation. This paper presents a simulation study that demonstrates a robust coordination strategy for the navigation of two heterogeneous robots, where one robot is the expert and the second robot is the helper in a vineyard. The robots are equipped with localization and navigation capabilities so that they can navigate the environment and appropriately position themselves in the work area. A modular collaborative algorithm is proposed for the coordinated navigation of the two robots in the field via a communications module. Furthermore, the robots are also able to position themselves accurately relative to each other using a vision module in order to effectively perform their cooperative tasks. For the experiments, a realistic simulation environment is considered, and the various control mechanisms are described. Experiments were carried out to investigate the robustness of the various algorithms and provide preliminary results before real-life implementation.

IoT System Selection as a Fuzzy Multi-Criteria Problem

This research aims to analyse the applications of IoT in agriculture and to compare the most widely used IoT platforms. The problem of determining the most appropriate IoT system depends on many factors, often expressed by incomplete and uncertain estimates. In order to find a feasible decision, this study develops a multi-criteria framework for IoT solution selection in a fuzzy environment. In the proposed framework, a new modification of the Multi-Attribute Border approximation Area Comparison (MABAC) method with a specific distance measure via intuitionistic fuzzy values has been presented as a decision analysis method. The new technique is more precise than existing crisp and fuzzy analogues, as it includes the three components of intuitionistic numbers (degree of membership, degree of non-membership and hesitancy degree) and the relationships between them. The effectiveness of the new decision-making framework has been verified through an illustrative example of ranking IoT platforms.

A bibliometric and thematic approach to agriculture 4.0

Researchers are developing digital solutions for agriculture. Humanity has perfected agriculture throughout history because this activity is fundamental to our existence. The agricultural sector is currently incorporating new technologies from other areas. This phenomenon is agriculture 4.0. However, a challenge to research is the integration of technologies from different knowledge fields, and this has caused theoretical and practical difficulties. Thus, our purpose with this study has been to understand the core agriculture 4.0 research themes. We have used a bibliometric analysis, and guided the data collection by the PRISMA protocol. VosViewer and Bibliometrix software generated the results. We found two main research fronts, one focussed on agriculture 4.0 development, and another on the impacts of agriculture 4.0, which may be positive or negative. We found 21 main keywords or topics researched in agriculture 4.0 related to these research fronts. These themes are within five different axes. We managed to establish a good understanding of the topics around agriculture 4.0. Future studies could focus on the responsible development of digital solutions for agriculture. This is because the social, environmental, and economic impacts of these new solutions may be positive or negative. We conclude that digital agriculture is the node technologies integration for the automation of agricultural activities.

Using Open Tools to Transform Retired Equipment into Powerful Engineering Education Instruments: A Smart Agri-IoT Control Example

People getting involved with modern agriculture should become familiar with and able to exploit the plethora of cutting-edge technologies that have recently appeared in this area. The contribution of the educational robotics in demystifying new scientific fields for K-12 students is remarkable, but things become more challenging when trying to discover efficient practices for higher education. Indeed, there is an apparent need for pilot examples facilitating students’ professional skills acquisition and thus matching the potential of the actual systems used in the modern agricultural premises. In this regard, this work discuses laboratory experiences while implementing an automatic airflow control system of convincing size and role capable for remote configuration and monitoring. This non-conventional robotic example exploits retired electromechanical equipment, from an old farm, and revives it using modern widely available microcontrollers, smart phones/tablets, network transceivers, motor drivers, and some cheap and/or custom sensors. The contribution of the corresponding software parts to this transformation is of crucial importance for the success of the whole system. Thankfully, these parts are implemented using easy-to-use programming tools, of open and free nature at most, that are suitable for the pairing credit-card-sized computer systems. The proposed solution is exhibiting modularity and scalability and assists students and future professionals to better understand the role of key elements participating in the digital transformation of the agricultural sector. The whole approach has been evaluated from both technical and educational perspective and delivered interesting results that are also reported.

Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning

Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops. However, disease screening is challenging as there are no clear visible symptoms on upper stems and leaves at early growth stages. The current manual screening method requires experts to observe the crown and roots of plants to detect disease, which is time-consuming, subjective, labor-intensive, and costly. As digital color imaging has the advantages of low cost and easy use, it has a high potential to be an economical solution for crown rot detection. In this research, a crown rot disease detection method was developed using a smartphone camera and machine learning technologies. Four common wheat varieties were grown in greenhouse conditions with a controlled environment, and all infected group plants were infected with crown rot without the presence of other plant diseases. We used a smartphone to take digital color images of the lower stems of plants. Using imaging processing techniques and a support vector machine algorithm, we successfully distinguished infected and healthy plants as early as 14 days after disease infection. The results provide a vital first step toward developing a digital color imaging phenotyping platform for crown rot detection to enable the management of crown rot disease effectively. As an easy-access phenotyping method, this method could provide support for researchers to develop an efficiency and economic disease screening method in field conditions.

Wheel Deflection Control of Agricultural Vehicles with Four-Wheel Independent Omnidirectional Steering

Due to the harsh working environment of wheeled agricultural vehicles in the field, it is difficult to ensure that all wheels make contact with the ground at the same time, which is easy to unequally distribute the yaw moments of each independent wheel. The commonly used vehicle lateral control methods are mostly controlled by coordinating the individual torque between different wheels. Obviously, this control method is not suitable for agricultural four-wheeled vehicles. The goal of this study was to provide a wheel steering angle control method that uses electric push rods as actuators that can cope with this problem. The design of a four-wheel steering controller generally adopts the linear PID control method, but the research object of this paper is difficult to establish an accurate and linear mathematical model due to the complex working environment. Therefore, fuzzy adjustment is added on the basis of PID control, which can meet the requirements of model difficulty and control accuracy at the same time. In order to verify the feasibility and rationality of the designed wheel steering mechanism, the model dynamics simulation based on ADAMS software and the response analysis of the electric linear actuator thrust were completed. Based on the kinematics model of the controlled object, the rotation angle of the actuator motor is used as the control target, the lateral deviation e and deviation variation ec are taken as input variables and the parameters KP, KI and KD are taken as output variables, thereby establishing a fuzzy PID controller. Then, this controller is constructed in the Matlab/ Simulink simulation environment to analyze the lateral deviation and response stability during the process of vehicle path tracking. From the verification results of the linear path walking test under the fuzzy PID control method, the maximum lateral deviation of vehicle chassis is 2.7 cm when the driving speed is set as 1 m/s, and the deviation adjustment stable time of the system is 0.15 s. It can be seen that the proposed steering control strategy has good response performance and effectively increases the steering stability.

Development of a Visual Servo System for Robotic Fruit Harvesting

One of the challenges in the future of food production, amidst increasing population and decreasing resources, is developing a sustainable food production system. It is anticipated that robotics will play a significant role in maintaining the food production system, specifically in labor-intensive operations. Therefore, the main goal of this project is to develop a robotic fruit harvesting system, initially focused on the harvesting of apples. The robotic harvesting system is composed of a six-degrees-of-freedom (DOF) robotic manipulator, a two-fingered gripper, a color camera, a depth sensor, and a personal computer. This paper details the development and performance of a visual servo system that can be used for fruit harvesting. Initial test evaluations were conducted in an indoor laboratory using plastic fruit and artificial trees. Subsequently, the system was tested outdoors in a commercial fruit orchard. Evaluation parameters included fruit detection performance, response time of the visual servo, and physical time to harvest a fruit. Results of the evaluation showed that the developed visual servo system has the potential to guide the robot for fruit harvesting.

Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass

Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

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.