Count data time series models and their applications

“Due to fast developments of advanced sensors, count data sets have become ubiquitous in many fields. Modeling and forecasting such time series have generated great interest. Modeling can shed light on the behavior of the count series and to see how they are related to other factors such as the environmental conditions under which the data are generated. In this research, three approaches to modeling such count data are proposed. First, a periodic autoregressive conditional Poisson (PACP) model is proposed as a natural generalization of the autoregressive conditional Poisson (ACP) model. By allowing for cyclical variations in the parameters of the model, it provides a way to explain the periodicity inherent in many count data series. For example, in epidemiology the prevalence of a disease may depend on the season. The autoregressive conditional Poisson hidden Markov model (ACP-HMM) is developed to deal with count data time series whose mean, conditional on the past, is a function of previous observations, with this relationship possibly determined by an unobserved process that switches its state or regime as time progresses. This model, in a sense, is the combination of the discrete version of the autoregressive conditional heteroscedastic (ARCH) formulation and the Poisson hidden Markov model. Both the above models address the frequently present serial correlation and the clustering of high or low counts observed in time series of count data, while at the same time allowing the underlying data generating mechanism to change cyclically or according to a hidden Markov process. Applications to empirical data sets show that these models provide a better fit than the standard ACP models. In addition to the above models, a modification of a zero-inflated Poisson model is used to analyze activity counts of the fruit fly. The model captures the dynamic structure of activity patterns and the fly\u27s propensity to sleep. The obtained results when fed to a convolutional neural network provides the possibility of building a predictive model to identify fruit flies with short and long lifespans”--Abstract, page iv

LifeCLEF 2016: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcom

Automated Identification of Insect Pests in Traps

Insect pests are a significant risk to the agricultural and horticultural sectors and present a major risk to many billions of dollars’ worth of food production. Significant infestations can threaten food security, as well as export markets due to importing countries placing restrictions on a region of origin. This study investigated the Mediterranean fruit fly (Medfly), species name Ceratitis capitata (Wiedemann). Fruit flies threaten a $12 billion industry and are widespread. Medfly is the most ubiquitous of the fruit fly pest species and therefore poses a significant threat to agriculture worldwide. The objectives of the project were to develop a low-cost and open source automated fruit fly trap, which could be used for remote monitoring of fruit fly traps. Due to various limitations the system could not be implemented as initially envisaged but a partially functional system was achieved. Some system components were successfully or partially implemented. Construction and remote operation of a Bosch BME280 temperature, pressure and relative humidity sensor, along with provision for data logging for this sensor was implemented. This system was successfully operated remotely via a WiFi connection. A 16MP camera was partially implemented but failed to perform as expected and further work on this hardware was abandoned. An Adafruit neopixel 12 LED ring was successfully implemented, to be used as a light source for image collection in the dark. Although the camera did not perform as expected, partial success was achieved in processing images by discriminating Medflies within a trap using the OpenCV Simple Blob Detector and saving the fly count to a csv file. No other easily implementable image processing options were found in OpenCV, which could discriminate fruit flies from background. A control algorithm was also developed to drive all the system components and to email a csv file with collated weather data and fly counts, with time stamps, to a designated email address. A housing for the entire system was designed and 3D printed at USQ and a partially operational prototype was constructed. The viability of the project is still considered to be feasible, although the work required and the challenges involved to achieve completion necessitate that much more time will need to be spent on the project to achieve a fully functional model, with potential for many years of further development. Obtaining a better camera and further development of the fruit fly identification software, power management, communication protocols and lighting system are all considered to be important

On computational models of animal movement behaviour

Finding structures and patterns in animal movement data is essential towards understanding a variety of behavioural phenomena, as well as shedding light into the relationships between animals among conspecifics and across different taxa with respect to their environments. The recent advances in the field of computational intelligence coupled with the proliferation of low-cost telemetry devices have made the gathering and analyses of behavioural data of animals in their natural habitat and in a wide range of context possible with aid of devices such as Global Positioning System (GPS). The sensory input that animals receive from their environment, and the corresponding motor output, as well as the neural basis of this relationship most especially as it affects movement, encode a lot of information regarding the welfare and survival of these animals and other organisms in nature's ecosystem. This has huge implications in the area of biodiversity monitoring, global health and understanding disease progression. Encoding, decoding and quantifying these functional relationships however can be challenging, boring and labour intensive. Artificial intelligence holds promise in solving some of these problems and even stand to benefit as understanding natural intelligence for instance can aid in the advancement of artificial intelligence. In this thesis, I investigate and propose several computational methods leveraging information theoretic metrics and also modern machine learning methods including supervised, unsupervised and a novel combination of both towards understanding, predicting, forecasting and quantifying a variety of animal movement phenomena at different time scales across different taxa and species. Most importantly the models proposed in this thesis tackle important problems bordering on human and animal welfare as well as their intersection. Crucially, I investigate several information theoretic metrics towards mining animal movement data, after which I propose machine learning and statistical techniques for automatically quantifying abnormal movement behaviour in sheep with Batten disease using unsupervised methods. In addition, I propose a predictive model capable of forecasting migration patterns in Turkey vulture as well as their stop-over decisions using bidirectional recurrent neural networks. And finally, I propose a model of sheep movement behaviour in a flock leveraging insights in cognitive neuroscience with modern deep learning models. Overall, the models of animal movement behaviour developed in this thesis are useful to a wide range of scientists in the field of neuroscience, ethology, veterinary science, conservation and public health. Although these models have been designed for understanding and predicting animal movement behaviour, in a lot of cases they scale easily into other domains such as human behaviour modelling with little modifications. I highlight the importance of continuous research in developing computational models of animal movement behaviour towards improving our understanding of nature in relation to the interaction between animals and their environments

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

Application of Artificial Intelligence algorithms to support decision-making in agriculture 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 in which the application of artificial intelligence algorithms, and particularly, of deep learning 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 for decision-making 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 sectors. Recent scientific developments in the field of deep learning, applied to agriculture, are reviewed and some challenges and potential solutions using deep learning algorithms in agriculture are discussed. 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. The ability of artificial neural networks, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM), to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size. The model achieved mean square error (MSE) values ranging from 0.07 to 0.27 (mm d–1)² for ETo (Reference Evapotranspiration) and 0.014 to 0.056 (m³m–3)² for SWC (Soil Water Content), with R2 values ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error (MSE) as loss function performed better than the model with other loss functions. Afterwards, the capabilities of these models and their extension, BLSTM and Bidirectional Gated Recurrent Units (BGRU) to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate endof- season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The BLSTM network outperformed the GRU, the LSTM, and the BGRU networks on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039 kg/ha. The performance of the BLSTM in the test was compared with the most commonly used deep learning method called CNN, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest regression. The BLSTM out-performed the other models with a R2-score between 0.97 and 0.99. The results show that analyzing agricultural data with the LSTM model improves the performance of the model in terms of accuracy. The CNN model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season. Additionally, a Deep Q-Network was trained for irrigation scheduling. The agent was trained to schedule irrigation for a tomato field in Portugal. Two LSTM models trained previously were used as the agent environment. One predicts the total water in the soil profile on the next day. The other one was employed to estimate the yield based on the environmental condition during a season and then measure the net return. The agent uses this information to decide the following irrigation amount. LSTM and CNN networks were used to estimate the Q-table during training. Unlike the LSTM model, the ANN and the CNN could not estimate the Qtable, and the agent’s reward decreased during training. The comparison of the performance of the model was done with fixed-base irrigation and threshold-based irrigation. The trained model increased productivity by 11% and decreased water consumption by 20% to 30% compared to the fixed method. Also, an on-policy model, Advantage Actor–Critic (A2C), was implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model A2C reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high importance in Portugal, such as fruit, cereals, and grapevines, which also have large water requirements. The models developed along this thesis can be re-evaluated and trained with historical data from other cultures with high production in Portugal, such as fruits, cereals, and grapes, which also have high water demand, to create a decision support and recommendation system that tells farmers when and how much to irrigate. This system helps farmers avoid wasting water without reducing productivity. This thesis aims to contribute to the future steps in the development of precision agriculture and agricultural robotics. The models developed in this thesis are relevant to support decision-making in agricultural activities, aimed at optimizing resources, reducing time and costs, and maximizing production.Nos últimos anos, a técnica de aprendizagem profunda (Deep Learning) foi aplicada com sucesso ao reconhecimento de imagem, reconhecimento de fala e processamento de linguagem natural. Assim, tem havido um incen tivo para aplicá-la também em outros sectores. O sector agrícola é um dos mais importantes, em que a aplicação de algoritmos de inteligência artificial e, em particular, de deep learning, precisa ser explorada, pois tem impacto direto no bem-estar humano. Em particular, há uma necessidade de explorar como os modelos de aprendizagem profunda para a tomada de decisão podem ser usados como uma ferramenta para cultivo ou plantação ideal, uso da terra, melhoria da produtividade, controlo de produção, de doenças, de pragas e outras atividades. A grande quantidade de dados recebidos de sensores em explorações agrícolas inteligentes (smart farms) possibilita o uso de deep learning como modelo para tomada de decisão nesse campo. Na agricultura, não há dois ambientes iguais, o que torna o teste, a validação e a implementação bem-sucedida dessas tecnologias muito mais complexas do que na maioria dos outros setores. Desenvolvimentos científicos recentes no campo da aprendizagem profunda aplicada à agricultura, são revistos e alguns desafios e potenciais soluções usando algoritmos de aprendizagem profunda na agricultura são discutidos. Maior desempenho em termos de precisão e menor tempo de inferência pode ser alcançado, e os modelos podem ser úteis em aplicações do mundo real. Por fim, são sugeridas algumas oportunidades para futuras pesquisas nesta área. A capacidade de redes neuronais artificiais, especificamente Long Short-Term Memory (LSTM) e LSTM Bidirecional (BLSTM), para modelar a evapotranspiração de referência diária e o conteúdo de água do solo é investigada. A aplicação destas técnicas para prever estes parâmetros foi testada em três locais em Portugal. Um BLSTM de camada única com 512 nós foi selecionado. A otimização bayesiana foi usada para determinar os hiperparâmetros, como taxa de aprendizagem, decaimento, tamanho do lote e tamanho do ”dropout”. O modelo alcançou os valores de erro quadrático médio na faixa de 0,014 a 0,056 e R2 variando de 0,96 a 0,98. Um modelo de Rede Neural Convolucional (CNN – Convolutional Neural Network) foi adicionado ao LSTM para investigar uma potencial melhoria de desempenho. O desempenho decresceu em todos os conjuntos de dados devido à complexidade do modelo. O desempenho dos modelos também foi comparado com CNN, algoritmos tradicionais de aprendizagem máquina Support Vector Regression e Random Forest. O LSTM obteve o melhor desempenho. Por fim, investigou-se o impacto da função de perda no desempenho dos modelos propostos. O modelo com o erro quadrático médio (MSE) como função de perda teve um desempenho melhor do que o modelo com outras funções de perda. Em seguida, são investigadas as capacidades desses modelos e sua extensão, BLSTM e Bidirectional Gated Recurrent Units (BGRU) para prever os rendimentos da produção no final da campanha agrícola. Os modelos usam dados históricos, incluindo dados climáticos, calendário de rega e teor de água do solo, para estimar a produtividade no final da campanha. A aplicação desta técnica foi testada para os rendimentos de tomate e batata em um local em Portugal. A rede BLSTM superou as redes GRU, LSTM e BGRU no conjunto de dados de validação. O modelo foi capaz de captar a relação não linear entre dotação de rega, dados climáticos e teor de água do solo e prever a produtividade com um MSE variando de 0,07 a 0,27 (mm d–1)² para ETo (Evapotranspiração de Referência) e de 0,014 a 0,056 (m³m–3)² para SWC (Conteúdo de Água do Solo), com valores de R2 variando de 0,96 a 0,98. O desempenho do BLSTM no teste foi comparado com o método de aprendizagem profunda CNN, e métodos de aprendizagem máquina, incluindo um modelo Multi-Layer Perceptrons e regressão Random Forest. O BLSTM superou os outros modelos com um R2 entre 97% e 99%. Os resultados mostram que a análise de dados agrícolas com o modelo LSTM melhora o desempenho do modelo em termos de precisão. O modelo CNN obteve o segundo melhor desempenho. Portanto, o modelo de aprendizagem profunda tem uma capacidade notável de prever a produtividade no final da campanha. Além disso, uma Deep Q-Network foi treinada para programação de irrigação para a cultura do tomate. O agente foi treinado para programar a irrigação de uma plantação de tomate em Portugal. Dois modelos LSTM treinados anteriormente foram usados como ambiente de agente. Um prevê a água total no perfil do solo no dia seguinte. O outro foi empregue para estimar a produtividade com base nas condições ambientais durante uma o ciclo biológico e então medir o retorno líquido. O agente usa essas informações para decidir a quantidade de irrigação. As redes LSTM e CNN foram usadas para estimar a Q-table durante o treino. Ao contrário do modelo LSTM, a RNA e a CNN não conseguiram estimar a tabela Q, e a recompensa do agente diminuiu durante o treino. A comparação de desempenho do modelo foi realizada entre a irrigação com base fixa e a irrigação com base em um limiar. A aplicação das doses de rega preconizadas pelo modelo aumentou a produtividade em 11% e diminuiu o consumo de água em 20% a 30% em relação ao método fixo. Além disso, um modelo dentro da táctica, Advantage Actor–Critic (A2C), é foi implementado para comparar a programação de irrigação com o Deep Q-Network para a mesma cultura de tomate. Os resultados mostram que o modelo de táctica A2C reduziu o consumo de água consumo em 20% comparado ao Deep Q-Network com uma pequena mudança na recompensa líquida. Estes modelos podem ser desenvolvidos para serem aplicados a outras culturas com elevada produção em Portugal, como a fruta, cereais e vinha, que também têm grandes necessidades hídricas. Os modelos desenvolvidos ao longo desta tese podem ser reavaliados e treinados com dados históricos de outras culturas com elevada importância em Portugal, tais como frutas, cereais e uvas, que também têm elevados consumos de água. Assim, poderão ser desenvolvidos sistemas de apoio à decisão e de recomendação aos agricultores de quando e quanto irrigar. Estes sistemas poderão ajudar os agricultores a evitar o desperdício de água sem reduzir a produtividade. Esta tese visa contribuir para os passos futuros na evolução da agricultura de precisão e da robótica agrícola. Os modelos desenvolvidos ao longo desta tese são relevantes para apoiar a tomada de decisões em atividades agrícolas, direcionadas à otimização de recursos, redução de tempo e custos, e maximização da produção.Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST). It was also 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

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

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

2023 - The Fourth Annual Fall Symposium of Student Scholars

The full program book from the Fall 2023 Symposium of Student Scholars, held in November 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1028/thumbnail.jp