AI Knowledge Transfer from the University to Society

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

Fruit Detection and Tree Segmentation for Yield Mapping in Orchards

Accurate information gathering and processing is critical for precision horticulture, as growers aim to optimise their farm management practices. An accurate inventory of the crop that details its spatial distribution along with health and maturity, can help farmers efficiently target processes such as chemical and fertiliser spraying, crop thinning, harvest management, labour planning and marketing. Growers have traditionally obtained this information by using manual sampling techniques, which tend to be labour intensive, spatially sparse, expensive, inaccurate and prone to subjective biases. Recent advances in sensing and automation for field robotics allow for key measurements to be made for individual plants throughout an orchard in a timely and accurate manner. Farmer operated machines or unmanned robotic platforms can be equipped with a range of sensors to capture a detailed representation over large areas. Robust and accurate data processing techniques are therefore required to extract high level information needed by the grower to support precision farming. This thesis focuses on yield mapping in orchards using image and light detection and ranging (LiDAR) data captured using an unmanned ground vehicle (UGV). The contribution is the framework and algorithmic components for orchard mapping and yield estimation that is applicable to different fruit types and orchard configurations. The framework includes detection of fruits in individual images and tracking them over subsequent frames. The fruit counts are then associated to individual trees, which are segmented from image and LiDAR data, resulting in a structured spatial representation of yield. The first contribution of this thesis is the development of a generic and robust fruit detection algorithm. Images captured in the outdoor environment are susceptible to highly variable external factors that lead to significant appearance variations. Specifically in orchards, variability is caused by changes in illumination, target pose, tree types, etc. The proposed techniques address these issues by using state-of-the-art feature learning approaches for image classification, while investigating the utility of orchard domain knowledge for fruit detection. Detection is performed using both pixel-wise classification of images followed instance segmentation, and bounding-box regression approaches. The experimental results illustrate the versatility of complex deep learning approaches over a multitude of fruit types. The second contribution of this thesis is a tree segmentation approach to detect the individual trees that serve as a standard unit for structured orchard information systems. The work focuses on trellised trees, which present unique challenges for segmentation algorithms due to their intertwined nature. LiDAR data are used to segment the trellis face, and to generate proposals for individual trees trunks. Additional trunk proposals are provided using pixel-wise classification of the image data. The multi-modal observations are fine-tuned by modelling trunk locations using a hidden semi-Markov model (HSMM), within which prior knowledge of tree spacing is incorporated. The final component of this thesis addresses the visual occlusion of fruit within geometrically complex canopies by using a multi-view detection and tracking approach. Single image fruit detections are tracked over a sequence of images, and associated to individual trees or farm rows, with the spatial distribution of the fruit counting forming a yield map over the farm. The results show the advantage of using multi-view imagery (instead of single view analysis) for fruit counting and yield mapping. This thesis includes extensive experimentation in almond, apple and mango orchards, with data captured by a UGV spanning a total of 5 hectares of farm area, over 30 km of vehicle traversal and more than 7,000 trees. The validation of the different processes is performed using manual annotations, which includes fruit and tree locations in image and LiDAR data respectively. Additional evaluation of yield mapping is performed by comparison against fruit counts on trees at the farm and counts made by the growers post-harvest. The framework developed in this thesis is demonstrated to be accurate compared to ground truth at all scales of the pipeline, including fruit detection and tree mapping, leading to accurate yield estimation, per tree and per row, for the different crops. Through the multitude of field experiments conducted over multiple seasons and years, the thesis presents key practical insights necessary for commercial development of an information gathering system in orchards

Sähköbussin nopeuden ja ohjauskulman säätö edellä ajavan ajoneuvon liike-radan seuraamisessa

Buses face problems when the capacity of a bus is limited but it should be larger to be able to carry more passengers. The capacity of a bus is already increased to its maximum that is allowed by the infrastructure. The capacity of a bus line could be increased by driving buses more frequently but it would increase costs, that is unwanted. Costs could be reduced by driving buses as platoons consisting of two buses where only the first bus would be operated by a professional driver and the second would be driven autonomously. Autonomous driving requires longitudinal and lateral control of a vehicle. The follower bus should be able to follow the path driven by the leader bus precisely and avoid inter-vehicular collisions while still driving as close together as possible to indicate other traffic that they move as a platoon. Lateral control is usually divided into path following and direct following methods in the literature. Path following methods include obtaining the path of the leader vehicle and following of that path. Path following methods are usually accurate in terms of lateral error but are complex and require a lot of computational capacity. Direct following methods are easy to compute but they do not guarantee precise path following. A simulation model consisting of two identical buses was developed. One longitudinal controller and four lateral control laws were proposed. Longitudinal controller was designed to work also in tight turns which is not usually investigated. Lateral control laws proposed were geometrical in nature and required only input as the relative position of the leader bus. Therefore, they did not require inter-vehicular communication. Longitudinal controller worked well for initialization of the system with inter-vehicular distances from 2 to 10 m. It worked well in acceleration and deceleration tests when both buses were loaded similarly but failed to prevent collisions when follower bus was loaded more heavily than the leader. In lateral controller tests, Pure Pursuit and Modified Pure Pursuit methods were able to follow the leader producing following lateral errors: 0,8 m and 1,1 m (steady-state tests), 0,8 m and 0,7 m (u-turn maneuver) and 0,3 m/0,4 m and 0,4 m/0,5 m (double lane change maneuver, 5 m/s/10 m/s respectively). Spline Pursuit method showed oscillatory behavior and did not follow the leader well. Circular Pursuit method showed also oscillatory behavior and did not follow the leader well. However, it showed better performance than the Spline Pursuit. It remains to be studied whether Pure Pursuit or Modified Pure Pursuit can challenge more sophisticated path following methods.Linja-autojen matkustajakapasiteetti on rajallinen, mikä aiheuttaa ongelmia, sillä sen tulisi olla suurempi. Kapasiteetti on jo nostettu suurimmalle mahdolliselle tasolle, mitä nykyinen infrastruktuuri mahdollistaa. Linja-autolinjan kapasiteettia voisi nostaa ajamalla linja-autoja tiheämmin. Tämä kuitenkin johtaa suurempiin kustannuksiin. Kustannuksia voisi vähentää ajamalla linja-autoja kahden ajoneuvon jonoina, joissa ensimmäistä ajo-neuvoa ohjaisi ammattilaiskuljettaja ja toinen olisi autonomisesti ohjattu. Autonominen ajaminen vaatii ajoneuvon nopeuden ja ohjauskulman säätöä. Seuraajalinja-auton pitää pystyä seuraamaan johtajalinja-auton ajamaa ajouraa tarkasti ja välttää törmäämistä johtajaan. Linja-autojen välinen etäisyys on kuitenkin oltava riittävän pieni, jotta se viestisi muulle liikenteelle, että ajoneuvot ajavat jonona. Kirjallisuus jakaa ohjauskulman säädön yleensä ajouran seuraamiseen ja suoraan seuraamiseen. Ajouran seuraaminen koostuu johtaja-ajoneuvon ajouran saamisesta ja tämän uran seuraamisesta. Ajouran seuraamisen metodit ovat yleensä tarkkoja poikittaisen virheen suhteen, mutta ovat monimutkaisia ja vaativat paljon laskennallista kapasiteettia. Suoran seuraamisen metodit ovat laskennallisesti kevyitä, mutta eivät takaa tarkkaa ajouran seuraamista. Kahdesta identtisestä linja-autosta koostuva simulaatiomalli kehitettiin. Yksi nopeussäädin ja neljä ohjauskulman säätölakia esitettiin. Nopeussäädin suunniteltiin toimimaan myös tiukoissa käännöksissä, mitä ei ole yleensä tutkittu. Ohjauskulman säätölait perustuivat geometriseen päättelyyn ja ne tarvitsivat vain johtajalinja-auton suhteellisen asentotiedon. Säätölait eivät vaatineet ajoneuvojen välistä kommunikaatiota. Nopeussäädin toimi järjestelmän alustamisessa ajoneuvojen välisen etäisyyden ollessa 2-10 m. Se toimi hyvin kiihdytys- ja jarrutustesteissä, kun molemmat linja-autot olivat lastattu identtisellä kuormalla, mutta epäonnistui estämään törmäämisen, kun seuraajalinja-auto oli lastattu suuremmalla kuormalla kuin johtaja. Ohjauskulman säädön testeissä Pure Pursuit ja Modified Pure Pursuit pystyivät seuraamaan johtajaa seuraavilla poikittaissuuntaisilla virheillä: 0,8 m ja 1,1 m (steady-state-testit), 0,8 m ja 0,7 m (u-käännös) ja 0,3 m/0,4 m ja 0,4 m/0,5 m (kaksoiskaistanvaihto, 5 m/s/10 m/s vastaavasti). Spline Pursuit käyttäytyi värähtelevästi eikä seurannut johtajaa hyvin. Circular Pursuit käyttäytyi värähtelevästi eikä seurannut johtajaa hyvin, mutta kuitenkin paremmin kuin Spline Pursuit. Jää nähtäväksi pystyykö Pure Pursuit tai Modified Pure Pursuit haastamaan monimutkaisempia ajouran seuraamisen metodeja

AI Knowledge Transfer from the University to Society

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting

Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detect and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the (x,y) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.Comment: 14 pages, 9 figure

Review on Automatic Variable-Rate Spraying Systems Based on Orchard Canopy Characterization

Pesticide consumption and environmental pollution in orchards can be greatly decreased by combining variable-rate spray treatments with proportional control systems. Nowadays, farmers can use variable-rate canopy spraying to apply weed killers only where they are required which provides environmental friendly and cost-effective crop protection chemicals. Moreover, restricting the use of pesticides as Plant Protection Products (PPP) while maintaining appropriate canopy deposition is a serious challenge. Additionally, automatic sprayers that adjust their application rates to the size and shape of orchard plantations has indicated a significant potential for reducing the use of pesticides. For the automatic spraying, the existing research used an Artificial Intelligence and Machine Learning. Also, spraying efficiency can be increased by lowering spray losses from ground deposition and off-target drift. Therefore, this study involves a thorough examination of the existing variable-rate spraying techniques in orchards. In addition to providing examples of their predictions and briefly addressing the influences on spraying parameters, it also presents various alternatives to avoiding pesticide overuse and explores their advantages and disadvantages

Lähestymistapa autonomiseen törmäyksenestoon Monorail-kuljetinjärjestelmässä

Collision Avoidance Systems are utilized both in industry and traffic in attempt to prevent material losses and injuries. These systems are implemented using sensors, communication systems or a combination of these. The most used sensors in collision avoidance applications are optical, electromagnetic and ultrasonic sensors. The communicationbased systems use both wireless and wired communications utilizing various protocols. This document describes the process of creating a prototype of a sensor-based Collision Avoidance System for Cimcorp Monorail Transfer system. Monorail Transfer is an automatic transportation system used in tire manufacturing plants for moving the tires between different process stations. It consists of a rail, which is either fastened into the roof of the plant or into a leg-like support structure, carriers which move along the rail and a cell controller. The aim is to prevent the collisions between the carriers and between carriers and other objects. The aim of the work is to have a functional prototype of an autonomous, sensor-based Collision Avoidance System which does not depend on Wi-Fi. The work begins with introducing the concept of Monorail Transfer in detail. Next the technologies behind the existing collision avoidance systems in industry and traffic are reviewed. The sensor types used in the implementations are identified and reviewed. Their suitability for the Monorail is considered. It is found that electromagnetic and optical sensors would be most suitable for the system. Electromagnetic sensors are discarded due to their high price and power consumption. Communication-based systems are reviewed. The Monorail Transfer’s current Collision Avoidance System is studied. After the theoretical part the new system is designed after defining its requirements. The sensors are chosen and reviewed. A Raspberry Pi 2 model B is chosen for pre-processing the sensor data prior to introducing it to the PLC’s in the Monorail Transfer. The Collision Avoidance software is programmed in Node.js, the communications between the PLC and Raspberry Pi are programmed in Node.js and the Graphical User Interface is implemented using HTML5, CSS and JS. The system is tested thoroughly and modified according to the findings. The prototype is found to be functional. Finally the document ends with conclusions about the implemented prototyp

HETEROGENEOUS MULTI-SENSOR FUSION FOR 2D AND 3D POSE ESTIMATION

Sensor fusion is a process in which data from different sensors is combined to acquire an output that cannot be obtained from individual sensors. This dissertation first considers a 2D image level real world problem from rail industry and proposes a novel solution using sensor fusion, then proceeds further to the more complicated 3D problem of multi sensor fusion for UAV pose estimation. One of the most important safety-related tasks in the rail industry is an early detection of defective rolling stock components. Railway wheels and wheel bearings are two components prone to damage due to their interactions with the brakes and railway track, which makes them a high priority when rail industry investigates improvements to current detection processes. The main contribution of this dissertation in this area is development of a computer vision method for automatically detecting the defective wheels that can potentially become a replacement for the current manual inspection procedure. The algorithm fuses images taken by wayside thermal and vision cameras and uses the outcome for the wheel defect detection. As a byproduct, the process will also include a method for detecting hot bearings from the same images. We evaluate our algorithm using simulated and real data images from UPRR in North America and it will be shown in this dissertation that using sensor fusion techniques the accuracy of the malfunction detection can be improved. After the 2D application, the more complicated 3D application is addressed. Precise, robust and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and SLAM. Each of different sensors employed to estimate the pose have their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this dissertation, a new approach to 3D pose estimation for a UAV in an unknown GPS-denied environment is presented. The proposed algorithm fuses the data from an IMU, a camera, and a 2D LiDAR to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a 2D LiDAR can only provide pose estimation in its scanning plane and thus it cannot obtain full pose estimation in a 3D environment. A novel method is introduced in this research that enables us to employ a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera. To the best of our knowledge 2D LiDAR has never been employed for 3D localization without a prior map and it is shown in this dissertation that our method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments