Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Chaotic price dynamics of agricultural commodities

Traditionally, commodity prices have been analyzed and modeled in the context of linear generating processes. The purpose of this dissertation is to address the adequacy of this work through examination of the critical assumption of independence in the residual process of linearly specified models. As an alternative, a test procedure is developed and utilized to demonstrate the appropriateness of applying generalized conditional heteroscedastic time series models (GARCH) to agricultural commodity prices. In addition, a distinction is made between testing for independence and testing for chaos in commodity prices. The price series of interest derive from the major international agricultural commodity markets, sampled monthly over the period 1960--1994. The results of the present analysis suggest that for bananas, beef, coffee, soybeans, wool and wheat seasonally adjusted growth rates, ARCH-GARCH models account for some of the non-linear dependence in these commodity price series. As an alternative to the ARCH-GARCH models, several neural network models were estimated and in some cases outperformed the ARCH family of models in terms of forecast ability. This further demonstrated the nonlinearity present in these time series. Although, further examination is needed, all prices were found to be non-linearly dependent. It was determined by use of different statistical measures for testing for deterministic chaos that wheat prices may be an example of such behavior. Therefore, their may be something to be gained in terms of short-run forecast accuracy by using semi-parametric modeling approaches as applied to wheat prices

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Contributions to time series analysis, modelling and forecasting to increase reliability in industrial environments.

356 p.La integración del Internet of Things en el sector industrial es clave para alcanzar la inteligencia empresarial. Este estudio se enfoca en mejorar o proponer nuevos enfoques para aumentar la confiabilidad de las soluciones de IA basadas en datos de series temporales en la industria. Se abordan tres fases: mejora de la calidad de los datos, modelos y errores. Se propone una definición estándar de métricas de calidad y se incluyen en el paquete dqts de R. Se exploran los pasos del modelado de series temporales, desde la extracción de características hasta la elección y aplicación del modelo de predicción más eficiente. El método KNPTS, basado en la búsqueda de patrones en el histórico, se presenta como un paquete de R para estimar datos futuros. Además, se sugiere el uso de medidas elásticas de similitud para evaluar modelos de regresión y la importancia de métricas adecuadas en problemas de clases desbalanceadas. Las contribuciones se validaron en casos de uso industrial de diferentes campos: calidad de producto, previsión de consumo eléctrico, detección de porosidad y diagnóstico de máquinas

Characterization and identification of poultry meat by non-destructive methods

Orientador: Douglas Fernandes BarbinTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: Atualmente a espectroscopia no infravermelho próximo (NIR) é utilizada na indústria agro-alimentar como uma técnica analítica não destrutiva, por ser rápida e dispensar a utilização de reagentes. No presente estudo, foi utilizada espectroscopia de infravermelho próximo (NIR) com um equipamento portátil e imagens hiperespectrais NIR (NIR-HSI) combinada com algoritmos de aprendizado de máquina e análise multivariada para a classificação e identificação de amostras de carnes moídas. Num primeiro trabalho, foram identificados diferentes partes de frango (peito, sobrecoxa e coxa) . As amostras de diferentes cortes de frango foram classificadas utilizando o NIR portátil combinados com algoritmos de machine learning (ML) e analises multivarida. Atributos físicos e químicos (características de cor, pH e L * a * b *) e composição química (proteína, gordura, umidade e cinzas) foram determinados para cada amostra (moidas e inteiras). Foram utilizados análise de componentes principais (PCA), algoritmos de Suport Vector Machine (SVM) e Random Forest (RF) e análises discriminantes (LDA) para a classificação das amostras. Os resultados confirmaram a possibilidade de diferenciar as amostras de peito, sobrecoxa e coxas com 97% de precisão, comprovando potencial deste método para diferenciar os cortes de frango. Num segundo trabalho, além das tecnologias mencionadas, foi usada a imagem RGB (RGB-I) para classificar três diferentes espécies de carne (frango, suína e bovina) e detectar diferentes quantidades de mistura entre elas. Os dados espectrais foram adquiridos para o NIR portátil no intervalo de comprimento de onda entre 900 e 1700 nm, enquanto para as imagens hiperespectrais no NIR foram entre 900 e 2500 nm. Para a classificação de diferentes espécies de carne moida, realizou-se PCA utilizando-se todas as varivéis e após seleção de variavéis latentes (VL), se realizou a LDA para classificar as amostras puras. Os dados brutos e pré-processados foram investigados separadamente como preditores dos modelos de regressão por mínimos quadrados parciais (PLSR). Além disso, este modelo utilizou as VL mais relevantes, com o objetivo de otimizar o processamento de dados. Os resultados de PLSR foram comparados usando coeficiente de determinação de previsão (R2p), relação do desempenho do desvio (RPD) e razão de intervalo do erro (RER). Os melhores resultados foram com NIR-HSI e RGB-I (R2p = 0,92, RPD = 3,82, RER = 15,77 e R2p = 0,86, RPD = 2,66, RER = 10,99 respectivamente). PCA e LDA aplicadas aos dados espectrais (NIR portátil e NIR-HSI) e nas VL (RGB-I) classificaram os três tipos de carne pura (frango, bovina e suína) com 100% de precisão. Finalmente, conclui-se que essas técnicas têm grande potencial para utilização na indústria de processamento de carnes e por instituições que realizam inspeções de segurança e qualidade dos alimentosAbstract: Near-infrared (NIR) spectroscopy is currently used in the agriculture and food industry as a non-destructive, fast and reagentless analytical technique. In the present study, the use of portable near-infrared (NIR) technology and NIR hyperspectral images combined with machine learning algorithms and multivariate statistical analysis were used to classify samples of different chicken cuts (breast, thigh, and drumstick). In addition to the mentioned technologies, the RGB (RGB-I) image was used to classify three different meat species (chicken, pork and beef) and to detect different amounts of mixture between them. The portable NIR spectral data were acquired in the wavelength range between 900 and 1700 nm, while the hyperspectral images were acquired between 900 and 2500 nm. The different chicken parts were classified using the portable NIR combined with machine learning algorithms (ML) and multivariate analyzes. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample (minced and non-minced). The spectral data exploited by principal component analysis (PCA), the algorithms of support vector machine (SVM) and random forest (RF) and linear discriminant analysis (LDA) were compared for the classification of chicken meat. Results confirmed the possibility of differentiating the breast samples, thighs and drumstick with 97% accuracy. PCA and LDA applied to spectral data (portable NIR and NIR-HSI) and the latent variables (RGB-I) classified 100% of the three types of pure ground meat (chicken, beef, pork). The results showed potential to use NIR portable spectrometer to differentiate the chicken parts and to classify meats of different species together with multivariate analysis. Regarding the classification of different meat species, PCA was performed on all variables and optimized on the latent variables selected with LDA to classify pure samples. Raw and preprocessed data were investigated separately as predictors of Partial Least Squares Regression (PLSR) models. In addition, this model was performed using the most relevant latent variables with the objective of optimizing data processing. Results of PLSR obtained to authenticate the chicken samples with the three spectroscopic techniques were compared using the coefficient of determination for prediction (R2p), ratio performance to deviation (RPD) and ratio of error range (RER). The best results were obtained with NIR-HSI and RGB-I (R2p = 0.92, RPD = 3.82, RER = 15.77 and R2p = 0.86, RPD = 2.66, RER = 10.99 respectively). Based on the results, these techniques can be used on-line by the meat processing industry and by institutions carrying out food safety and quality inspectionsDoutoradoEngenharia de AlimentosDoutora em Engenharia de AlimentosCAPE

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems