A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.info:eu-repo/semantics/publishedVersio

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

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 publishing the results, among others

Data-centric Design and Training of Deep Neural Networks with Multiple Data Modalities for Vision-based Perception Systems

224 p.Los avances en visión artificial y aprendizaje automático han revolucionado la capacidad de construir sistemas que procesen e interpreten datos digitales, permitiéndoles imitar la percepción humana y abriendo el camino a un amplio rango de aplicaciones. En los últimos años, ambas disciplinas han logrado avances significativos,impulsadas por los progresos en las técnicas de aprendizaje profundo(deep learning). El aprendizaje profundo es una disciplina que utiliza redes neuronales profundas (DNNs, por sus siglas en inglés) para enseñar a las máquinas a reconocer patrones y hacer predicciones basadas en datos. Los sistemas de percepción basados en el aprendizaje profundo son cada vez más frecuentes en diversos campos, donde humanos y máquinas colaboran para combinar sus fortalezas.Estos campos incluyen la automoción, la industria o la medicina, donde mejorar la seguridad, apoyar el diagnóstico y automatizar tareas repetitivas son algunos de los objetivos perseguidos.Sin embargo, los datos son uno de los factores clave detrás del éxito de los algoritmos de aprendizaje profundo. La dependencia de datos limita fuertemente la creación y el éxito de nuevas DNN. La disponibilidad de datos de calidad para resolver un problema específico es esencial pero difícil de obtener, incluso impracticable,en la mayoría de los desarrollos. La inteligencia artificial centrada en datos enfatiza la importancia de usar datos de alta calidad que transmitan de manera efectiva lo que un modelo debe aprender. Motivada por los desafíos y la necesidad de los datos, esta tesis formula y valida cinco hipótesis sobre la adquisición y el impacto de los datos en el diseño y entrenamiento de las DNNs.Específicamente, investigamos y proponemos diferentes metodologías para obtener datos adecuados para entrenar DNNs en problemas con acceso limitado a fuentes de datos de gran escala. Exploramos dos posibles soluciones para la obtención de datos de entrenamiento, basadas en la generación de datos sintéticos. En primer lugar, investigamos la generación de datos sintéticos utilizando gráficos 3D y el impacto de diferentes opciones de diseño en la precisión de los DNN obtenidos. Además, proponemos una metodología para automatizar el proceso de generación de datos y producir datos anotados variados, mediante la replicación de un entorno 3D personalizado a partir de un archivo de configuración de entrada. En segundo lugar, proponemos una red neuronal generativa(GAN) que genera imágenes anotadas utilizando conjuntos de datos anotados limitados y datos sin anotaciones capturados en entornos no controlados

An IoT System for Converting Handwritten Text to Editable Format via Gesture Recognition

Evaluation of traditional classroom has led to electronic classroom i.e. e-learning. Growth of traditional classroom doesn’t stop at e-learning or distance learning. Next step to electronic classroom is a smart classroom. Most popular features of electronic classroom is capturing video/photos of lecture content and extracting handwriting for note-taking. Numerous techniques have been implemented in order to extract handwriting from video/photo of the lecture but still the deficiency of few techniques can be resolved, and which can turn electronic classroom into smart classroom. In this thesis, we present a real-time IoT system to convert handwritten text into editable format by implementing hand gesture recognition (HGR) with Raspberry Pi and camera. Hand Gesture Recognition (HGR) is built using edge detection algorithm and HGR is used in this system to reduce computational complexity of previous systems i.e. removal of redundant images and lecture’s body from image, recollecting text from previous images to fill area from where lecture’s body has been removed. Raspberry Pi is used to retrieve, perceive HGR and to build a smart classroom based on IoT. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. In text conversion, recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, graphs and figures are included with recognition of word, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT, the editable format of lecture notes is given to students via desktop application which helps students to edit notes and images according to their necessity

An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

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

Robottiautojen tutkimukseen tarkoitetun virtuaalisen koneoppimisympäristön suorituskyvyn evaluointi

While automotive manufacturers are already implementing Autonomous Driving (AD) features in their latest commercial vehicles, fully automated vehicles are still not a reality. In addition to AD, recent developments in mobile networks enables the possibility of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. Vehicle-to-Everything (V2X) communication, or vehicular Internet of Things (IoT), can provide solutions that improve the safety and efficiency of traffic. Both AD and vehicular IoT need improvements to the surrounding infrastructure and vehicular hardware and software. The upcoming 5G network not only reduces latency, but improves availability and massively increases the amount of supported simultaneous connections, making vehicular IoT a possibility. Developing software for AD and vehicular IoT is difficult, especially because testing the software with real vehicles can be hazardous and expensive. The use of virtual environments makes it possible to safely test the behavior of autonomous vehicles. These virtual 3D environments include physics simulation and photorealistic graphics. Real vehicular hardware can be combined with these simulators. The vehicle driving software can control the virtual vehicle and observe the environment through virtual sensors, such as cameras and radars. In this thesis we investigate the performance of such simulators. The issue with existing open-source simulators is their insufficient performance for real-time simulation of multiple vehicles. When the simulation is combined with real vehicular hardware and edge computing services, it is important that the simulated environment resembles reality as closely as possible. As driving in traffic is very latency sensitive, the simulator should always be running in real-time. We select the most suitable traffic simulator for testing these multi-vehicle driving scenarios. We plan and implement a system for distributing the computational load over multiple computers, in order to improve the performance and scalability. Our results show that our implementation allows scaling the simulation by increasing the amount of computing nodes, and therefore increasing the number of simultaneously simulated autonomous vehicles. For future work, we suggest researching how the distributed computing solution affects latency in comparison to a real-world testing environment. We also suggest the implementation of an automated load-balancing system for automatically scaling the simulation to multiple computation nodes based on demand.Vaikka uusimmista automalleista löytyy jo itsestään ajavien autojen ominaisuuksia, robottiautot vaativat vielä runsaasti kehitystä ennen kuin ne kykenevät ajamaan liikenteessä täysin itsenäisesti. Robottiautojen ohella ajoneuvojen ja infrastruktuurin välinen (V2X) kommunikaatio ja tuleva 5G mobiiliverkkoteknologia sekä mobiiliverkkojen tukiasemien yhteyteen sijoitettavat laskentapilvet mahdollistavat liikenteen turvallisuuden ja sujuvuuden parantamisen. Tätä V2X kommunikaatiota voidaan esimerkiksi hyödyntää varoittamalla ajoneuvoja nurkan takaa tulevista pyöräilijöistä, jalankulkijoista ja huonoista tieolosuhteista. Robottiautojen ja V2X kommunikaation hyödyntämistä on hankala tutkia oikeassa liikenteessä. Fyysisten autojen ja tieverkostoa ympäröivän infrastruktuurin rakentaminen on kallista, lisäksi virhetilanteista johtuvat onnettomuudet voivat aiheuttaa henkilö- ja tavaravahinkoja. Yksi ratkaisu on virtuaalisten testausympäristöjen käyttö. Tällaiset simulaattorit kykenevät mallintamaan ajoneuvojen käyttäytymistä reaaliaikaisen fysiikkamoottorin avulla ja tuottamaan valokuvamaista grafiikkaa simulaatioympäristöstä. Robottiauton ohjelmisto voi hallita simuloidun auton käyttäytymistä ja havainnoida simuloitua ympäristöä virtuaalisten kameroiden ja tutkien avulla. Tässä diplomityössä tutkitaan liikennesimulaattorien suorituskykyä. Avoimen lähdekoodin simulaattorien ongelmana on niiden huono skaalautuvuus, eikä niiden suorituskyky riitä simuloimaan useita autoja reaaliajassa. Tässä diplomityössä tehdään lyhyt katsaus olemassa oleviin simulaattoreihin, joiden joukosta valitaan parhaiten yllämainittujen ongelmien tutkimiseen soveltuva simulaattori. Simulaattorin suorituskyvyn ja skaalautuvuuden parantamiseksi suunnitellaan järjestelmä, joka hajauttaa simulaattorin työkuorman useammalle laskentapisteelle. Kyseinen järjestelmä toteutetaan ja sen toimivuutta testataan mittaamalla. Mittaustulokset osoittavat, että hajautettu laskenta parantaa simulaattorin suorituskykyä ja että reaaliaikaisesti simuloitujen autojen lukumäärää voidaan kasvattaa lisäämällä laskentapisteiden lukumäärää. Jatkotutkimukseksi ehdotetaan tutkimaan simulaation hajauttamisen vaikutusta viiveisiin, ja kuinka simulaattorin aiheuttamat ylimääräiset viiveet suhtautuvat tosielämän viiveisiin. Lisäksi suositellaan automaattisen kuormituksentasaajan toteuttamista, jonka avulla simulaatiota voidaan automaattisesti hajauttaa useille laskentapisteille tarvittavan laskentakapasiteetin mukaisesti