Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics

This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field

Integrasjon av et minimalistisk sett av sensorer for kartlegging og lokalisering av landbruksroboter

Robots have recently become ubiquitous in many aspects of daily life. For in-house applications there is vacuuming, mopping and lawn-mowing robots. Swarms of robots have been used in Amazon warehouses for several years. Autonomous driving cars, despite being set back by several safety issues, are undeniably becoming the standard of the automobile industry. Not just being useful for commercial applications, robots can perform various tasks, such as inspecting hazardous sites, taking part in search-and-rescue missions. Regardless of end-user applications, autonomy plays a crucial role in modern robots. The essential capabilities required for autonomous operations are mapping, localization and navigation. The goal of this thesis is to develop a new approach to solve the problems of mapping, localization, and navigation for autonomous robots in agriculture. This type of environment poses some unique challenges such as repetitive patterns, large-scale sparse features environments, in comparison to other scenarios such as urban/cities, where the abundance of good features such as pavements, buildings, road lanes, traffic signs, etc., exists. In outdoor agricultural environments, a robot can rely on a Global Navigation Satellite System (GNSS) to determine its whereabouts. It is often limited to the robot's activities to accessible GNSS signal areas. It would fail for indoor environments. In this case, different types of exteroceptive sensors such as (RGB, Depth, Thermal) cameras, laser scanner, Light Detection and Ranging (LiDAR) and proprioceptive sensors such as Inertial Measurement Unit (IMU), wheel-encoders can be fused to better estimate the robot's states. Generic approaches of combining several different sensors often yield superior estimation results but they are not always optimal in terms of cost-effectiveness, high modularity, reusability, and interchangeability. For agricultural robots, it is equally important for being robust for long term operations as well as being cost-effective for mass production. We tackle this challenge by exploring and selectively using a handful of sensors such as RGB-D cameras, LiDAR and IMU for representative agricultural environments. The sensor fusion algorithms provide high precision and robustness for mapping and localization while at the same time assuring cost-effectiveness by employing only the necessary sensors for a task at hand. In this thesis, we extend the LiDAR mapping and localization methods for normal urban/city scenarios to cope with the agricultural environments where the presence of slopes, vegetation, trees render the traditional approaches to fail. Our mapping method substantially reduces the memory footprint for map storing, which is important for large-scale farms. We show how to handle the localization problem in dynamic growing strawberry polytunnels by using only a stereo visual-inertial (VI) and depth sensor to extract and track only invariant features. This eliminates the need for remapping to deal with dynamic scenes. Also, for a demonstration of the minimalistic requirement for autonomous agricultural robots, we show the ability to autonomously traverse between rows in a difficult environment of zigzag-liked polytunnel using only a laser scanner. Furthermore, we present an autonomous navigation capability by using only a camera without explicitly performing mapping or localization. Finally, our mapping and localization methods are generic and platform-agnostic, which can be applied to different types of agricultural robots. All contributions presented in this thesis have been tested and validated on real robots in real agricultural environments. All approaches have been published or submitted in peer-reviewed conference papers and journal articles.Roboter har nylig blitt standard i mange deler av hverdagen. I hjemmet har vi støvsuger-, vaske- og gressklippende roboter. Svermer med roboter har blitt brukt av Amazons varehus i mange år. Autonome selvkjørende biler, til tross for å ha vært satt tilbake av sikkerhetshensyn, er udiskutabelt på vei til å bli standarden innen bilbransjen. Roboter har mer nytte enn rent kommersielt bruk. Roboter kan utføre forskjellige oppgaver, som å inspisere farlige områder og delta i leteoppdrag. Uansett hva sluttbrukeren velger å gjøre, spiller autonomi en viktig rolle i moderne roboter. De essensielle egenskapene for autonome operasjoner i landbruket er kartlegging, lokalisering og navigering. Denne type miljø gir spesielle utfordringer som repetitive mønstre og storskala miljø med få landskapsdetaljer, sammenlignet med andre steder, som urbane-/bymiljø, hvor det finnes mange landskapsdetaljer som fortau, bygninger, trafikkfelt, trafikkskilt, etc. I utendørs jordbruksmiljø kan en robot bruke Global Navigation Satellite System (GNSS) til å navigere sine omgivelser. Dette begrenser robotens aktiviteter til områder med tilgjengelig GNSS signaler. Dette vil ikke fungere i miljøer innendørs. I ett slikt tilfelle vil reseptorer mot det eksterne miljø som (RGB-, dybde-, temperatur-) kameraer, laserskannere, «Light detection and Ranging» (LiDAR) og propriopsjonære detektorer som treghetssensorer (IMU) og hjulenkodere kunne brukes sammen for å bedre kunne estimere robotens tilstand. Generisk kombinering av forskjellige sensorer fører til overlegne estimeringsresultater, men er ofte suboptimale med hensyn på kostnadseffektivitet, moduleringingsgrad og utbyttbarhet. For landbruksroboter så er det like viktig med robusthet for lang tids bruk som kostnadseffektivitet for masseproduksjon. Vi taklet denne utfordringen med å utforske og selektivt velge en håndfull sensorer som RGB-D kameraer, LiDAR og IMU for representative landbruksmiljø. Algoritmen som kombinerer sensorsignalene gir en høy presisjonsgrad og robusthet for kartlegging og lokalisering, og gir samtidig kostnadseffektivitet med å bare bruke de nødvendige sensorene for oppgaven som skal utføres. I denne avhandlingen utvider vi en LiDAR kartlegging og lokaliseringsmetode normalt brukt i urbane/bymiljø til å takle landbruksmiljø, hvor hellinger, vegetasjon og trær gjør at tradisjonelle metoder mislykkes. Vår metode reduserer signifikant lagringsbehovet for kartlagring, noe som er viktig for storskala gårder. Vi viser hvordan lokaliseringsproblemet i dynamisk voksende jordbær-polytuneller kan løses ved å bruke en stereo visuel inertiel (VI) og en dybdesensor for å ekstrahere statiske objekter. Dette eliminerer behovet å kartlegge på nytt for å klare dynamiske scener. I tillegg demonstrerer vi de minimalistiske kravene for autonome jordbruksroboter. Vi viser robotens evne til å bevege seg autonomt mellom rader i ett vanskelig miljø med polytuneller i sikksakk-mønstre ved bruk av kun en laserskanner. Videre presenterer vi en autonom navigeringsevne ved bruk av kun ett kamera uten å eksplisitt kartlegge eller lokalisere. Til slutt viser vi at kartleggings- og lokaliseringsmetodene er generiske og platform-agnostiske, noe som kan brukes med flere typer jordbruksroboter. Alle bidrag presentert i denne avhandlingen har blitt testet og validert med ekte roboter i ekte landbruksmiljø. Alle forsøk har blitt publisert eller sendt til fagfellevurderte konferansepapirer og journalartikler

Determination of Time Dependent Stress Distribution on Potato Tubers at Mechanical Collision

This study focuses on determining internal stress progression and the realistic representation of time dependent deformation behaviour of potato tubers under a sample mechanical collision case. A reverse engineering approach, physical material tests and finite element method (FEM)-based explicit dynamics simulations were utilised to investigate the collision based deformation characteristics of the potato tubers. Useful numerical data and deformation visuals were obtained from the simulation results. The numerical results are presented in a format that can be used for the determination of bruise susceptibility magnitude on solid-like agricultural products. The modulus of elasticity was calculated from experimental data as 3.12 [MPa] and simulation results showed that the maximum equivalent stress was 1.40 [MPa] and 3.13 [MPa] on the impacting and impacted tubers respectively. These stress values indicate that bruising is likely on the tubers. This study contributes to further research on the usage of numerical-methods-based nonlinear explicit dynamics simulation techniques in complicated deformation and bruising investigations and industrial applications related to solid-like agricultural products

An economic analysis of a robotic harvest technology in New Zealand fresh apple industry : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agribusiness, Massey University School of Agriculture and Environment, Manawatu, New Zealand

The New Zealand apple industry is predominately an export-oriented industry relying on manual labour throughout the year. In recent years, however, labour shortages for harvesting have been jeopardising its competitiveness and profitability. Temporary immigration labour programs, such as the Recognised Seasonal Employer (RSE) program have not been able to solve the labour shortages, urging the industry to consider use of harvesting automation, i.e. robotic technology, as a solution. Harvesting robots are still in commercial trial stage and no studies have assessed the economic feasibility of such technology. The present study for the first time develops a bio-economic model to analyse the investment decision for adopting harvesting robots compared to available alternatives, i.e. platform and manual harvesting systems, using net present value (NPV) as the method of analysis; for newly established single-, bi-, and multi-varietal orchards across different orchard sizes, and three apple varieties (Envy, Jazz, and Royal Gala); and implications of orchard canopy transition and associated sensitivities are considered. The results of the analysis identified fruit value and yield as the key drivers for the adoption of harvesting automation. For relatively low value and or yielding varieties such as Jazz or Royal Gala, robots are less profitable in single-varietal orchard compared to bi-varietal orchard planted with relatively low value and yielding varieties. In a multi-varietal orchard, a relatively high value and high yield variety, such as Envy, is crucial to compensate for the costs incurred for harvesting other varieties using robots or platforms. The greatest potential benefit of utilising harvesting robots was reducing pickers required by an average of 54% for Envy and 48% for each of Jazz and Royal Gala across all orchard sizes compared to manual harvesting; and 7% in average for each of Envy, Jazz, and Royal Gala across all orchard sizes compared to platform harvesting system. This study also identified the break-even price for a robotic harvester in a single-varietal orchard, showed that the break-even prices exceeded the assumed price of the robot, and are highly variable depending on the varietal value and yield, where Envy as a relatively higher value and yielding variety returns a break-even price of $2.92 million compared to relatively lower value and yielding varieties, Jazz with$674,895, and Royal Gala with $689,608. Sensitivity analyses showed that both harvesting speed and efficiency are key parameters in the modelled orchard and positively affected the net returns of the investment and must be considered by researchers and manufacturers. However, for developers and potential adopters of robots, it should be more important that robots operate faster, but not necessarily as more efficient in order to generate a high return while substituting the highest number of pickers and leaving less unharvested fruit on trees in the limited harvesting window. Reducing robot price by 12% and 42% can generate an equivalent level of profit similar to manual or platform harvesting, respectively. Increases in labour wages, and decreases in labour availability and efficiency adversely affected the NPV and profitability outlook of the investment, but NPV was more affected by the decreases in labour efficiency and availability than wage increases. This research has important science and policy implications for policy makers, academics, growers, engineers, and manufacturers. From an economic perspective, for late adopters or those growers who may not be financially able to invest in robots or may be uncertain about their performance, platform harvesting system can be utilised as an alternative solution that is commercially available until robotic harvesting technology improves or becomes more affordable, and commercially available. Alternatively, it may be possible for these orchardists to benefit from utilising the robotic harvester in the form of a co-operative or contract-harvesting business model to avoid the capital costs associated with purchasing and operating the robots. Besides the economic factors, robotic harvesters have the potential to be considered as a solution for non-economic factors such as food safety problems. This is more apparent in the post-Covid-19 pandemic era, which has not only made it more difficult for growers to source their required workers due to border closures, but also has led consumers to be more cautious about food safety when they make purchase decisions and prefer to have their fresh fruit touchless from farm to plate. This may not be a problem for packhouses as most are automated, but it may be an issue for harvesting operations, because pickers have to pick apples by hand. Even though robots cannot be the only option for growers to rely on for the foreseeable future as they are not commercially available, in the current situation robot harvesting may be the most ideal solution

The digitization of agricultural industry – a systematic literature review on agriculture 4.0

Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities

Smart electrostatic crop spraying using remote sensing technology

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonFor this thesis, smart spraying robot was designed, constructed and tested to validate the concept of smart pest control. Electrostatic charging of sprayed pesticide was realized in a spray nozzle design that improved plant coverage and reduced wasted pesticide as well as soil pollution. A thorough investigation into electrostatic spraying was conducted, which was accompanied by extensive simulations and experimentation. The results obtained from the simulation experimentation on industry standard electrostatic spray system (ESS) nozzles along with laboratory testing of these nozzles, detecting spray coverage using water sensitive paper and additional optical spray visualization methods gave the necessary insight and experience required to develop a new spray nozzle. Additional COMSOL simulation and experimentation were carried out on a Fan Hydraulic Spray Nozzle (FHSN), the results of which allowed for the effective addition of electrostatic induction capabilities, thereby transforming the (FHSN) into Electrostatic Induction Spray Nozzle (EISN) which is one of the prime parts of the smart spraying system. SOLIDWORKS software was used in the designing parts of this nozzle which were then manufactured using a 3D printer. An AL05D robotic manipulator and a TTRK tracked platform from Lynxmotion ™ were the mini mobile robot components selected for the feasibility study of the smart electrostatic crop spraying system. This mobile robot was equipped with a CCD digital camera, a range detector, and path mark detector to provide the necessary sensors required by the smart electrostatic spray system. A Windows™ based mobile computer in addition to an ARDUINO™ based orksmicrocontroller system were chosen to provide the computational power required by the system. These were arranged in a master – slave configuration, with the main processing for images and motion being conducted inside the master computer using programs created by Matlab smart ™ software. The execution of motion commands and the operation of the range and path mark detection along with operating the spray nozzle were performed on the slave computer using C as the programming language. The manufactured smart electrostatic spray system moves along cotton crop rows with a camera that scans the selected plant for pest infestation on the upper and lower surfaces of plant leaves. When a pest is detected, the spray nozzle is targeted on it at the appropriate distance, and a burst of pesticide destroys it. The results of experiments have shown that using the electrostatic induction system improves coverage 3 to 4 fold and reduces soil contamination by 2 to 4 fold. The system has plenty of room for performance improvement, and future development will make it adaptable for application to other crops and applications.The Iraqi government, the Ministry of Higher Education and Scientific Research and Kerbala Universit

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others