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Item type:Publication, Access status: with files , Low-cost design proposal of a modular telescopic autonomous agricultural robot(Multidisciplinary Digital Publishing Institute (MDPI), 2026-04-16); ; ; ; The application of autonomous robotics in food production is still in its infancy but bears significant potential, especially in the sector of precision farming. Here we present the results of a survey conducted during the years 2022–2023 with local farmers in Northern Germany to identify the challenges that are being faced by both conventional and organic farming practices due to climate change and increased food production regulations in the EU region. Additionally, a pilot study with a medium-sized (Demeter) farm is presented, identifying the real needs and problems organic farmers face during their food production chain from seeding, weeding, and maintaining the equipment till bringing in the harvest. The results indicate a strong demand for modular, autonomous, and digitized solutions to address key challenges in agricultural production. To address these challenges we propose a novel mechanical robotic platform design that has been developed in accordance with the Fab City principles of open and local production resulting in a low-cost open-source solution.Publicationtype: Journal Article16 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Die HOOU an der TUHH : Erfahrungen und Erfolge 2020In 2020, 16 projects were sponsored and implemented within the Hamburg Open Online University at the University of Technology Hamburg. In this booklet, the lessons learnt within these projects are consolidated.Publicationtype: Text2052 408 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Plant and weed identifier robot as an agroecological tool using artificial neural networks for image identification(Multidisciplinary Digital Publishing Institute (MDPI), 2021-03-08); ; Farming systems form the backbone of the world food system. The food system, in turn, is a critical component in sustainable development, with direct linkages to the social, economic, and ecological systems. Weeds are one of the major factors responsible for the crop yield gap in the different regions of the world. In this work, a plant and weed identifier tool was conceptualized, developed, and trained based on artificial deep neural networks to be used for the purpose of weeding the inter-row space in crop fields. A high-level design of the weeding robot is conceptualized and proposed as a solution to the problem of weed infestation in farming systems. The implementation process includes data collection, data pre-processing, training and optimizing a neural network model. A selective pre-trained neural network model was considered for implementing the task of plant and weed identification. The faster R-CNN (Region based Convolution Neural Network) method achieved an overall mean Average Precision (mAP) of around 31% while considering the learning rate hyperparameter of 0.0002. In the plant and weed prediction tests, prediction values in the range of 88–98% were observed in comparison to the ground truth. While as on a completely unknown dataset of plants and weeds, predictions were observed in the range of 67–95% for plants, and 84% to 99% in the case of weeds. In addition to that, a simple yet unique stem estimation technique for the identified weeds based on bounding box localization of the object inside the image frame is proposed.Publicationtype: Journal ArticleScopus© Citations 24 180 817 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , HOOU an der TUHH : Jahresbericht 2020The annual report depicts the activities of the Hamburg Open Online University (HOOU) at the University of Technology Hamburg for the year 2020. Therein, the status quo as well as the current development within the various fields of action are being portrayed while also showcasing publications and events undertaken by the HOOU@TUHH. Furthermore, the progress of the individual projects is described.Publicationtype: Project Deliverable685 284