Automated Early Detection of Drops in Commercial Egg Production Using Neural Networks

[Abstract] 1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of cost-sensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/04

Determination of Egg Storage Time at Room Temperature Using a Low-Cost NIR Spectrometer and Machine Learning Techniques

[Abstract] Currently, consumers are more concerned about freshness and quality of food. Poultry egg storage time is a freshness and quality indicator in industrial and consumer applications, even though egg marking is not always required outside the European Union. Other authors have already published works using expensive laboratory equipment in order to determine the storage time and freshness of eggs. This paper presents a novel alternative method based on low-cost devices for the rapid and non-destructive prediction of egg storage time at room temperature (23 ± 1 °C). H&N brown flock with 49-week-old hens were used as a source for the sampled eggs. Samples were scanned for a period of 22 days beginning from the time the egg was laid. The spectral acquisition was performed using a low-cost near-infrared reflectance (NIR) spectrometer which has a wavelength range between 740 nm and 1070 nm. The resulting dataset of 660 samples was randomly split according to a 10-fold cross-validation in order to be used in a contrast and optimization process of two machine learning algorithms. During the optimization, several models were tested to develop a robust calibration model. The best model used a Savitzky Golay pre-processing technique with a third derivative order and an artificial neural network with ten neurons in one hidden layer. Regressing the storage time of the eggs, tests achieved a coefficient of determination (R-squared) of 0.8319 ± 0.0377 and a root mean squared error in cross-validation test set (RMSECV) of 1.97 days. Although further work is needed, this technique shows industrial potential and consumer utility to determine an egg's freshness using a low-cost spectrometer connected to a smartphone

Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

[Abstract] Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%.This work is part of DINTA-UTMACH and RNASA-UDC research groups. This work is partially supported by Instituto de Salud Carlos III, grant number PI17/01826. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49; IN845D-2020/03]. Another source of support was the CYTED network (PCI2018\_093284) funded by the Spanish Ministry of Innovation and ScienceXunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2018/49Xunta de Galicia; IN845D-2020/0

ESTIMAÇÃO DE ALTURA, VOLUME E AFILAMENTO DE ÁRVORES DE EUCALIPTO UTILIZANDO MÁQUINA DE VETOR DE SUPORTE

Diante de restrições no uso de florestas inequiâneas, o investimento em florestas equiâneas tem sido cada vez maior. Isso torna crescente o interesse em estudos buscando maximizar o rendimento em volume de madeira dessas florestas. O crescimento desse setor tem impulsionado a busca por métodos mais acurados na estimação de variáveis dendrométricas a fim de obter estimativas confiáveis a respeito do estoque volumétrico dos povoamentos florestais. Este trabalho teve como objetivo avaliar a exatidão da estimação da altura total, volume e afilamento do fuste de árvores de eucalipto, utilizando Máquina de Vetor de Suporte (MVS). Para a realização deste trabalho, foram utilizados dados provenientes de um povoamento de eucalipto. A base de dados foi dividida, aleatoriamente, em dois grupos: 70% para ajuste dos modelos de regressão e treinamento da MVS e 30% para validação. Foram ajustadas diferentes configurações de MVS e diferentes modelos de regressão. A avaliação dos modelos de regressão e das configurações da MVS foi baseada nas estatísticas: coeficiente de correlação, raiz quadrada do erro quadrático médio e viés. Com base nos resultados obtidos, foi observado que a MVS proporcionou maior exatidão nas estimativas da altura total, volume individual e afilamento do fuste em relação aos modelos clássicos de regressão utilizados. Palavras-chave: Modelos de Regressão, Aprendizagem de Máquinas, Variáveis Dendrométricas

Estudio de aplicabilidad de técnicas de inteligencia artificial en el sector agropecuario

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] O aprendizaje máquina é unha rama da intelixencia artificial (IA) que utiliza algoritmos para realizar tarefas, sen que se teña programado explícitamente. Para o seu funcionamento require un proceso de formación e validación baseado en exemplos. Nesta tese proponse estudar a aplicabilidade dalgunhas técnicas de IA na produción agrícola. A tese é apoiada por tres publicacións cun importante factor de impacto JCR. Dous deles fan referencia a unha base de datos de produción de aves de ovos e outra a unha base de datos sobre a industrialización da cana de azucre. Na produción avícola estas técnicas foron estudadas para a alerta precoz dos problemas na curva de produción. En canto á aplicación destas técnicas no proceso industrial de cana de azucre, optimizáronse os modelos de calibración dos espectros NIR para o control de calidade nunha fábrica de azucre. Usáronse máquinas de soporte vectorial e redes neuronais artificiais. A aplicación destas técnicas ten un alto potencial de uso na produción agrícola, xa que posibilita o desenvolvemento de sistemas intelixentes de apoio ás decisións produtivas.[Resumen] El aprendizaje máquina es una rama de la inteligencia artificial (IA) que utiliza algoritmos para realizar tareas, sin que hayan sido programados de manera explícita. Para su funcionamiento se requiere de un proceso de entrenamiento y validación en base a ejemplos. En esta Tesis Doctoral, se propone estudiar la aplicabilidad de algunas técnicas de IA en la producción agropecuaria. El trabajo está respaldado por tres publicaciones con un importante factor de impacto JCR. Dos de ellas se refieren a una base de datos de producción avícola de huevos y la otra, a una base de datos de la industrialización de la caña de azúcar. En la producción avícola estas técnicas fueron estudiadas para la alerta temprana de problemas en la curva de producción. En cuanto a la aplicación de estas técnicas en el proceso industrial de la caña de azúcar, se optimizó los modelos de calibración de los espectros NIR para el control de calidad en una fábrica de azúcar. Se utilizó Máquinas de Soporte Vectorial y Redes de Neuronas Artificiales. La aplicación de estas técnicas tiene un alto potencial de uso en la producción agropecuaria, ya que posibilita el desarrollo de sistemas inteligentes de apoyo a las decisiones productivas[Abstract] Machine learning is a branch of artificial intelligence that uses algorithms to perform tasks, without having been programmed explicitly. For its operation requires a process of training and validation based on examples. In this thesis the application of artificial intelligence techniques in agricultural production is studied. As main result of the thesis, three articles has been published in journals with important JCR impact factors. Two of them refer to a database of poultry production of eggs and the other to a database of the industrialization of sugar cane. In poultry production these techniques were studied for the early warning of problems in the production curve. For the application of these techniques in the industrial process of sugarcane, the calibration models of the NIR spectra for the quality control in a sugar factory were optimized. In this work were used Support Vector Machines and Artificial Neural Networks. The application of these techniques has a high potential of use in the agricultural production, since it opens up the development of intelligent systems to support productive decisions

Modeli za potporu pri odlučivanju o raspoloživosti zrakoplova temeljem dubinske analize podataka

Proces održavanja zrakoplova karakteriziraju velike količine strukturiranih i nestrukturiranih podataka koji se svakodnevno bilježe u obliku pilotskih izvještaja, izvještaja o aktivnostima održavanja, zapisa o nezgodama i zastojima, izvještaja nakon leta, itd. Znanja dobivena na temelju povijesnih podataka se mogu koristiti za unapređenje procesa održavanja zrakoplova, no ekstrakcija tih znanja se ne može obaviti ručno. U tu svrhu se koriste tehnike dubinske analize podataka, koje omogućavaju automatiziranu ili polu-automatiziranu ekstrakciju znanja iz skupova podataka. Predloženo istraživanje bavi se razvojem modela, temeljenih na tehnikama dubinske analize podatka, koji će služiti kao potpora pri odlučivanju o raspoloživosti zrakoplova, a pri modeliranju se koriste tri izvora podataka: podaci prikupljeni iz sustava za nadzor tehničke ispravnosti stanja zrakoplova, zapisi o nepravilnostima u radu sustava/prošlim kvarovima i zapisi o zastojima u eksploataciji zrakoplova (operacijskim zastojima). Podaci su prikupljeni u razdoblju od 76 uzastopnih mjeseci za četiri zrakoplova tipa Airbus A319/20 koji pripadaju floti jednog zračnog prijevoznika. U disertaciji su analizirani kritični sustavi zrakoplova; sustav automatskog leta (ATA 22), sustav upravljanja letjelicom (ATA 27), hidraulički sustav (ATA 29), sustav podvozja (ATA 32) i navigacijski sustav (ATA 34). Klasifikacijski modeli prve grupe izgrađeni su s ciljem predviđanja nastanka pilotskog upisa u tehničku knjigu na temelju prethodno generiranih poruka upozorenja u određenim fazama leta. Razvijen je postupak za strukturiranje podataka prikupljenih iz sustava za nadzor tehničke ispravnosti zrakoplova te postupak integracije tih podataka sa zapisima o nepravilnostima u radu sustava/prošlim kvarovima. Udruživanjem skupova podataka za izgradnju klasifikacijskih modela prve grupe otkriven je rang relevantnih značajki primjenom različitih filterskih postupaka (korelacijskog postupka, gini indeks postupka, informacijskog dobitka i omjera informacijskog dobitka) za selekciju značajki. Dodatno je provedeno istraživanje kako smanjenje udjela ulaznih značajki utječe na učinkovitost modela (F-mjeru, osjetljivosti i specifičnosti) za različite načine uzorkovanja podataka (uravnoteženo i slučajno uzorkovanje). Klasifikacijski modeli druge grupe izgrađeni su s ciljem predviđanja posljedice nastalih tekstualnih pilotskih zapisa na raspoloživost zrakoplova. Izgradnji modela prethodila je aktivnost udruživanja skupova podataka o nepravilnostima u radu sustava/kvarova i operacijskim zastojima. Usvajanjem evaluacijske mjere točnosti, modeli su uspoređeni s postojećim modelom iz sličnih istraživanja te je dokazana njihova primjenjivost