Machine Learning in Action: Exploring Examples in Multiple Domains

Machine learning, a subset of artificial intelligence, is an exciting and fast-moving field in the context of the Fourth Industrial Revolution. Machine learning studies computer algorithms that use a variety of approaches to automatically learn from experience and improve the prediction of a target state, without explicit programming. In this presentation, you will learn about the theoretical foundations of machine learning and how to apply it to solve relevant problems. Special attention will be given to describing the potential of deep learning. This presentation will be illustrated with examples from research projects on geoscience, medicine 4.0, and other areas of interest

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

(c) The Author/sTo apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset.fals

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset

Uso de sensor multiespectral en arándano Vaccinium corymbosum L. y sus aplicaciones en agricultura de precisión

Universidad Nacional Agraria La Molina. Facultad de Agronomía. Departamento Académico de FitotecniaEl presente trabajo consistió en hacer uso de la teledetección en agricultura de precisión para dar a conocer indicadores de vegetación y poder detectar niveles de estrés de cualquier tipo en el cultivo de arándano cultivado convencionalmente. El uso de la tecnología en la agricultura en los últimos años se viene aplicando a mayor escala, la teledetección mediante un sensor multiespectral es uno de ellos que se utiliza para obtener el índice de vegetación de diferencia normalizada por sus siglas en inglés (NDVI), para determinar el NDVI se hace uso de las longitudes de onda que son absorbidas y reflejadas por las hojas de la planta en las bandas del rojo (RED) e infrarrojo cercano (NIR), mediante la siguiente relación se obtiene los valores del NDVI = (NIR-RED) /(NIR+RED). La captura de estas longitudes de onda se obtuvo con una cámara con sensores multiespectrales que sobrevoló la superficie mediante un Dron de ala fija. Los valores de esta relación de longitudes de onda varían desde -1 hasta 1, siendo los valores negativos referente a objetos inertes y valores cercanos a la unidad indican alta densidad de vegetación. Las imágenes se procesaron mediante una computadora, obteniendo una imagen en mosaico donde se ubicó zonas con bajo índice de vegetación, las zonas con bajo vigor representa un grado de estrés de cualquier tipo. Se revisó en campo y se identificó en la zona radicular capas de materia orgánica sin descomponer, presencia de Anomala spp. en el cuello y raíz de planta. También mediante NDVI se calculó el índice de área verde que fue complemento base para incorporar otros sistemas de detección, que relaciona la cantidad de volumen de aplicación fitosanitaria con el área de follaje. Obteniendo menor volumen de aplicación en zonas de menor área verde y viceversa.The present work consisted of making use of remote sensing in precision agriculture to publicize vegetation indicators and to detect stress levels of any kind in conventionally grown blueberry cultivation. The use of technology in agriculture in recent years has been applied on a larger scale, remote sensing through a multispectral sensor is one of them that is used to obtain the normalized difference vegetation index (NDVI), to determine the NDVI is made use of the wavelengths that are absorbed and reflected by the leaves of the plant in the red bands (RED) and near-infrared (NIR), by means of the following relationship the values of NDVI = (NIR-RED) / (NIR+RED) are obtained. The capture of these wavelengths was obtained with a camera with multispectral sensors that flew over the surface by a fixedwing drone. The values of this wavelength ratio vary from -1 to 1, with negative values referring to inert objects and values close to unity indicating high vegetation density. The images were processed by a computer, obtaining a mosaic image where areas with low vegetation index were located, the areas with low vigor represents a degree of stress of any kind. It was reviewed in the field and layers of undecomposed organic matter were identified in the root zone, presence of Anomala spp. in the neck and root of the plant. Also by NDVI was calculated the green area index that was a base complement to incorporate other detection systems, which relates the amount of volume of phytosanitary application with the area of foliage. Obtaining less volume of application in areas of less green area and vice versa

Gerenciamento das barreiras no desenvolvimento da agricultura 4.0 na cadeia de produção agrícola da região Sul do Brasil

A agricultura 4.0 atualiza os métodos tradicionais de produção e as estratégias agrícolas mundiais para uma cadeia de valor otimizada usando uma variedade de tecnologias emergentes que aprimoram soluções disruptivas em todas as etapas da cadeia de produção agrícola. Devido à complexidade do ecossistema agrícola em mudança, os benefícios da nova revolução tecnológica não serão compartilhados uniformemente. É necessário compreender os problemas e desafios que precisam ser enfrentados para que mais países se beneficiem plenamente do potencial da agricultura 4.0. No Brasil, o cenário de desenvolvimento da agricultura 4.0 é complexo e pouco se sabe sobre as reais barreiras que causam impacto na sua adoção entre os atores da cadeia de produção agrícola. Faltam estudos sobre a percepção dos agricultores brasileiros com relação às barreiras que podem comprometer o caminho bem-sucedido da agricultura 4.0 neste setor. Esta tese investiga como pode ser realizado o gerenciamento das barreiras que dificultam o desenvolvimento da agricultura 4.0 na cadeia de produção agrícola da região Sul do Brasil. Para isso, esta tese foi organizada em três artigos, os quais objetivam: (i) realizar uma revisão sistemática da literatura com a finalidade de identificar as descrições, tecnologias, barreiras, vantagens e desvantagens da agricultura 4.0; (ii) validar as barreiras identificadas na literatura que dificultam o desenvolvimento da agricultura 4.0 na cadeia de produção agrícola; e (iii) explorar as inter-relações das barreiras no desenvolvimento da agricultura 4.0 na cadeia de produção agrícola da região Sul do Brasil, para sustentar a proposição de um framework abrangente que busque gerenciá-las. Como resultado esperado, está a proposição de um framework que vai ajudar na gestão de estratégias para a adoção e desenvolvimento da agricultura 4.0 na cadeia de produção agrícola da região Sul do Brasil. A abordagem de pesquisa desta tese inclui o uso de mixed-method (qualitativo - Revisão Sistemática da Literatura, Interpretive Structural Modeling, Matrix Impact of Cross Multiplication Applied to Classification, e quantitativo - Análise Fatorial Confirmatória, Interpretive Structural Modeling). Os principais resultados podem ser sumarizados como: (i) compilado das descrições da agricultura 4.0, proposição de uma definição abrangente do termo para a cadeia de produção agrícola, identificação e classificação de 25 barreiras em dimensões (tecnológica, econômica, política, social e ambiental), apresentação das tecnologias, vantagens, desvantagens, tendências, e agenda de pesquisa; (ii) validação de 25 barreiras, apresentação e discussão das barreiras mais frequentes e importantes apontadas pelos agricultores (falta de infraestrutura, falta de soluções acessíveis aos agricultores, necessidade de fomentar P&D e modelos de negócios inovadores, risco de faixa etária e falta de eficácia nos dados sobre o meio rural); e iii) identificação das barreiras no desenvolvimento da agricultura 4.0 que possuem alto poder de condução e as que são dependentes, proposta de um framework para gerenciá-las. Como conclusão, do ponto de vista acadêmico, esses resultados ajudam os atores da cadeia de produção agrícola a abrir caminho para o desenvolvimento bem-sucedido da agricultura 4.0. A pesquisa também corrobora para ampliar o debate inclusivo que pode moldar socialmente a introdução da agricultura 4.0. Do ponto de vista prático, os resultados ajudam os formuladores de políticas a elaborar estratégias mais bem detalhadas que busquem ampliar e difundir a agricultura 4.0 neste setor.Agriculture 4.0 upgrades traditional production methods and world agriculture strategies to an optimized value chain using a range of emerging technologies that enhance disruptive solutions at all stages of the agricultural production chain. Due to the complexity of the changing farm ecosystem, the new technological revolution's benefits will not be shared evenly. It is necessary to understand the problems and challenges that need to be addressed so that more countries can fully benefit from the potential of agriculture 4.0. In Brazil, the development scenario of agriculture 4.0 is complex, and little is known about the real barriers that impact its adoption among actors in the agricultural production chain. Empirical studies are lacking on the perception of Brazilian farmers regarding the barriers that may compromise the success of agriculture 4.0 in this sector. This thesis investigates how the management of barriers that hinder the development of agriculture 4.0 in the agricultural production chain in the southern region of Brazil can be carried out. For this, this thesis was organized into three papers, which aim to: (i) carry out a systematic review of the literature to identify the descriptions, technologies, barriers, advantages, and disadvantages of agriculture 4.0; (ii) to validate the barriers identified in the literature that hinder the development of agriculture 4.0 in the agricultural production chain; and (iii) explore the interrelationships of barriers in the development of agriculture 4.0 in the agricultural production chain in southern Brazil, to support the proposition of a comprehensive framework that seeks to manage them. As an expected result, a framework was proposed that will help in the management of strategies for the adoption and development of agriculture 4.0 in the agricultural production chain in the southern region of Brazil. The research approach of this thesis includes the use of mixedmethod (qualitative - Systematic Literature Review, Interpretive Structural Modeling, Matrix Impact of Cross Multiplication Applied to Classification, and quantitative - Confirmatory Factor Analysis, Interpretive Structural Modeling). The main results can be summarized as (i) compiled from descriptions of agriculture 4.0, a proposition of a comprehensive definition of the term for the agricultural production chain, identification, and classification of 25 barriers in dimensions (technological, economic, political, social and environmental), presentation of technologies, advantages, disadvantages, trends, and research agenda; (ii) validation of 25 barriers, presentation and discussion of the most frequent and important barriers identified by farmers (lack of infrastructure, lack of solutions accessible to farmers, need to encourage R&D and innovative business models, age group risk and lack of effectiveness in data on rural areas); and iii) identification of barriers in the development of agriculture 4.0 that have a high driving power and those that are dependent, the proposal of a framework to manage them, proposal of a framework to manage them. In conclusion, from a theoretical point of view, these results help actors in the agricultural production chain to pave the way for the successful development of agriculture 4.0. The research also corroborates to broaden the inclusive debate that can socially shape the introduction of agriculture 4.0. From a practical point of view, the results help policymakers develop more detailed strategies to expand and spread agriculture 4.0 in this sector

Energy Transition and Climate Change in Decision-making Processes

There is a growing concern about the climate; numerous voices stress that, in order to overcome the climate crisis, the transition to a low-carbon society is the most reasonable path to follow. In this type of society, individuals would be characterized by making mindful efforts to drastically decrease carbon and greenhouse gas emissions, and promote benign energy sources. In order to facilitate this transition, a social perspective in addition to technological, political and economic aspects must be integrated into the relevant decision-making processes. This is necessary because the public can strongly affect actions aimed at driving profound changes in traditional energy systems. To contribute to the effort of promoting energy transition, the Editors of this book invited scholars and practitioners conducting research in the areas of climate change and the energy transition to submit their work. This book includes studies that establish a valuable source of information which can be used to enhance decision-making processes which, in turn, can turn the energy transition into reality. Hopefully, efforts such as this collection of knowledge can help economies make a step towards a secure and sustainable energy future in which renewables will have replaced the centuries-long human dependence on fossil fuels