Towards autonomy in agriculture: Design and prototyping of a robotic vehicle with seed selector

Traditional method of seeding by farmers demands laborious work and is now becoming story of the past. Technological revolution in mechatronics and allied areas is reshaping the agricultural processes, making the robots an integral part of this automation. This paper presents design details of an autonomous robot developed keeping in view the constraints imposed by an agricultural field. The novelty of the proposed low-cost indigenously developed modular vehicle lies in design of its seed selector. The simple but efficient mechanism of the single seed selector with extremely low miss rate distinguishes the vehicle from other designs. Hardware details including sensing, actuation, processing and communication modules and software architecture are detailed in the paper. Results of trajectory tracking obtained by implementing the proposed scheme on a mini-robot and functionality of seed selector demonstrate potential of the presented robotic vehicle

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

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

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

Artificial Intelligence based Robotic Platforms for Autonomous Precision Agriculture

Robotic applications are continuously expanding into every aspect of human livelihood, it becomes paramount to leverage this trend for precision agriculture. The agricultural sector despite being an important sector for human is slowly evolving in terms of technology. Crude and manual processes which are conventionally used for agriculture have severe economic and social impacts. The inefficiencies and less productiveness of these methods results to food wastage amidst food shortage, inconsistencies, time consumption, higher labour expenses, and low yield. The world will benefit from automating the processes in agriculture. In bid of addressing such, it becomes necessary to build on existing platforms and develop intelligent autonomous vehicles for precision agriculture. This should include development of intelligent drones for precision agriculture, development of intelligent ground robots for precision agriculture, and other systems working cooperatively. To achieve this, we leverage on Artificial Intelligence (AI) and mathematical methods to impact sufficient intelligence on robotic platforms to make them suitable for precision agriculture. This thesis explores the capabilities of AI for weed classification and detection, weed relative position estimation, fruit 6D pose estimation and virtual reality for teleoperated systems in fruit picking. Infestation of weeds diminishes the yield of crops in agriculture. Deep learning is becoming a more popular approach for identifying weeds on farmlands. However, precision agriculture requires that the object of interest (weed) is precisely classified and detected to facilitate removal or spraying. An approach for this is presented and involves cascading a classification network (ResNet-50) with a detection network (YOLO) for weed classification and detection which we termed Fused-YOLO. Thus, weeds can precisely be located and classified (type) within an image frame. Inspired by the precision of this detection model, the work extends to presenting a novel monocular vision-based approach for drones to detect multiple types of weeds and estimate their positions autonomously for precision agriculture applications. A drone is subjected to an elliptical trajectory while acquiring images from an onboard monecular camera. The images are fed to the fused-YOLO model in real-time. The centre of the detection bounding boxes is leveraged to be the centre of the detected object of interest (weeds). The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV and are effectively used in an estimation scheme that adopts the Unscented Kalman Filteration to estimate the exact relative positions of the weeds. The robustness of this algorithm allows for both indoor and outdoor implementation while achieving a competitive result with affordable off-the-shelf sensors. Artificial intelligence for autonomous 6D pose estimation has valuable contributions to agricultural practices rallying around fruit picking, harvesting, remote operations and other contact-related applications. Conventionally, Convolutional Neural Networks (CNNs) based approaches are adopted for pose estimation. However, precision agriculture applications are demanding on higher accuracy at lower computational costs for real-time applications. Motivated by this, a novel architecture called Transpose is proposed based on transformers. TransPose is an improved Transformer-based 6D pose estimation with a depth refinement. More modalities often result in higher accuracy at the expense of computational cost. TransPose takes in a single RGB image as input without extra modality. However, an innovative light-weight depth estimation network architecture is incorporated into the model to estimate depth from an RGB image using a feature pyramid with an up-sampling method. A transformer model having proven to be efficient, regress the 6D pose directly and also outputs object patches. The depth and the patches are utilised to further refine the regressed 6D pose. The performance of the model is extensively assessed and compared with state-of-the-art methods. As part of this research, a first-ever fruit-oriented 6D pose dataset was acquired. Lastly, a seamless teleoperation pipeline that interfaces virtual reality with robots for precision agriculture tasks is proposed to pave the way for virtual agriculture. This utilises the Transpose model to estimate the 6D pose of a fruit and render it in a virtual reality environment. A robotic manipulator is which is then controlled from within the virtual reality environment to pick/harvest the fruit while being guided by the Transpose AI model. The robustness of the pipeline is tested over simulation and real-time implementation with a physical robotic manipulator is also investigated

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Cyber-Human Systems, Space Technologies, and Threats

CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development

Emerging Technologies

This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book