Comparison of modelling techniques for milk-production forecasting

peer-reviewedThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions

Redes neurais artificiais aplicadas na estimativa do índice de área foliar utilizando imagens de sensoriamento remoto / Artificial neural networks applied to estimating the leaf area index using remote sensing images

Objetivou-se com esse estudo, obter o Índice de Área Foliar (IAF) por meio de Redes Neurais Artificiais (RNAs) tendo como dados de entrada o Índice de Vegetação por Diferença Normalizada (NDVI) obtido por meio de imagens de sensoriamento remoto. O estudo foi realizado em área comercial de 35 ha de tomate industrial, irrigado por pivô central, no município de Vila Propício, Goiás. Os dados utilizados para o treinamento da RNA foram obtidos in loco e por sensoriamento remoto utilizando imagens dos sensores OLI/Landsat 8 e MSI/Sentinel-2 (A e B). Para universalizar a utilização da RNA escolhida, as coordenadas utilizadas no treinamento ou na entrada da rede foram obtidas pela diferença das coordenadas X e Y, e altitude de cada ponto em relação ao centro do pivô, respectivamente. Após o treinamento, foi feita a validação externa das RNAs, metodologia na qual se apresenta dados novos para a rede. A melhor RNA treinada teve o coeficiente de determinação (R2) geral de 0,74 e o EQM geral menor que 4%. Com base no R2 obtido entre o IAF estimado e o aferido, o emprego de sistemas computacionais inteligentes na estimava do IAF é viável

Self-learning modules for spectra evaluation

Monitoring milk processing is an essential task as it affects the quality and safety of the final product. The aim of this investigation was to develop and analyse the self-learning system for the supervision of the processing of milk. In the self-learning evaluation module, several algorithms for data analysis of near infrared (NIR) and Raman spectra was implemented for the prediction of sample quality and safety. In the first part of this thesis, the use of NIR spectroscopy for controlling milk processing was investigated. For this reason, a high-quality quartz flow cell with a 1 mm pathlength including temperature controlling option for liquids was implemented. For sample preparation, UHT-milk with 1.5 % fat content was measured at 5 °C and considered as the reference milk. Samples with various changes such as added water and cleaning solution, different fat content and temperature as well as milks from various suppliers were investigated as the modified samples. A data set from reference and modified samples was obtained with NIR measurements. In this study, first Savitzky-Golay derivative with second polynomial order and window size of 15 was applied. It was compared with the usefulness of raw spectrum and also the combination of raw and first derivative spectrum. For the self-learning sector, an autoencoder neural network was employed. Within this thesis, it was shown that the trained autoencoder using first derivative spectra was capable to detect 5 % added water and 9 % cleaning solution in the milk. However, by using the combination spectra, the difference of 0.1 % in fat concentration was perfectly recognized. These two procedures were able to detect milks from different suppliers and difference of 10 °C in the measurement temperature. Another part of this work was done using Raman spectroscopy. The aim of this part was to check if the previous result can be improved. In this step, the circulation method was again employed the same as in the previous part. However, because of the heat introduced to the sample by the laser using in Raman spectroscopy and the length of plastic tubes which can be affected by the temperature of the laboratory, the measurement temperature was kept at 10 °C. 1.5 % fat UHT milk was utilized as the reference sample. Milks with various changes such as different fat contents, various measurement temperatures and added water or cleaning solution were investigated as the modified samples. In this investigation, not only the autoencoder but also some chemometric models were utilized with the purpose of anomaly detection. Principal component analysis (PCA) was investigated to check if the various samples can becategorized separately. In addition, two chemometric modelling techniques such as principal component regression and Gaussian process regression were tested to check the ability for change detection. By using the results obtained by PCA, a sufficient categorization of various samples was not achieved. While the PCR did not present a promising prediction as the related R2 was 0.7, Gaussian process regression with R2 of 0.97 predicted the changes almost perfectly. The trained autoencoder and Gaussian process regression both were able to define 5 % water and cleaning solution, difference of 0.1 % fat content, and variation of 5 °C in the measurement temperature. In comparison between the autoencoder and Gaussian process regression, it should be mentioned that the Gaussian process regression was capable to determine more abnormal signals than the autoencoder, however, it must be trained with all the possible changes. In contrast, the autoencoder can be trained once just with reference signals and used in online monitoring properly. As the final part and to detect which type of anomalies happened during the milk processing, several classification approaches such as linear discriminant analysis, decision tree, support vector machine, and k nearest neighbour were utilized. While decision trees and linear discriminant analysis failed to effectively characterize the various types of anomalies, the k nearest neighbor and support vector machine presented promising results. The support vector machine presented an accuracy of 81.4 % for test set, while the k nearest neighbor showed an accuracy of 84.8 %. As a result, it is reasonable to assume that both algorithms are capable of classifying various groups of data accurately. It can help the milk business figure out whats going wrong during the processing of milk. In general, Raman spectroscopy produced better findings than NIR spectroscopy, because the typical absorption bands of milk components in NIR spectrometers may be impacted by high water absorption combined with substantial light scattering by fat globules. Additionally, the autoencoder as self-learning system was capable of correctly detecting changes during milk processing, however, classification algorithms can aid in obtaining more details.Die Überwachung der Milchverarbeitung ist eine wesentliche Aufgabe, da sie die Qualität und Sicherheit des Endprodukts beeinflusst. Das Ziel dieser Untersuchung war die Entwicklung und Analyse eines selbstlernenden Systems zur Überwachung der Milchverarbeitung. In dem selbstlernenden Auswertungsmodul wurden verschiedene Algorithmen zur Datenanalyse implementiert, um die Qualität und Sicherheit der Proben mit Hilfe spektroskopischer Methoden vorherzusagen. Im ersten Teil dieser Arbeit wurde der Einsatz der Nahinfrarot-Spektroskopie (NIR) zur Kontrolle der Milchverarbeitung untersucht. Zu diesem Zweck wurde eine hochwertige Quarzdurchflusszelle mit einer Schichtdicke von 1 mm und einer Temperiermöglichkeit für Flüssigkeiten eingesetzt. Zur Probenvorbereitung wurde UHT-Milch mit 1,5 % Fettgehalt bei 5 °C gemessen und als Referenzmilch betrachtet. Als modifizierte Proben wurden Proben mit verschiedenen Veränderungen wie Wasser- und Reinigungsmittelzusatz, unterschiedlichem Fettgehalt und Temperatur sowie Milch von verschiedenen Lieferanten untersucht. Mit NIR Messungen wurde ein Datensatz von Referenz- und modifizierten Proben gewonnen. In dieser Studie wurde die erste Savitzky-Golay-Ableitung mit zweiter Polynomordnung und einer Fenstergröße von 15 verwendet. Sie wurde mit der Auswertegüte des Rohspektrums und auch der Kombination aus Roh- und erstem Ableitungsspektrum verglichen. Für den selbstlernenden Bereich wurde ein neuronales Netz als Autoencoder eingesetzt. Im Rahmen dieser Arbeit wurde gezeigt, dass der trainierte Autoencoder unter Verwendung der ersten Ableitung in der Lage war, 5 % zugesetztes Wasser und 9 % Reinigungslösung in der Milch zu erkennen. Durch die Verwendung der Kombinationsspektren wurde auch der Unterschied von 0,1 % in der Fettkonzentration perfekt erkannt. Diese beiden Verfahren waren in der Lage, Milch von verschiedenen Lieferanten und einem Unterschied von 10 °C bei der Messtemperatur zu erkennen. Ein weiterer Teil dieser Arbeit wurde mit der Raman-Spektroskopie durchgeführt. Ziel dieses Teils war es, zu prüfen, ob das vorherige Ergebnis verbessert werden kann. In diesem Schritt wurde wieder die gleiche Zirkulationsmethode wie im vorherigen Teil verwendet. Wegen der Wärme, die durch den Laser bei der Raman-Spektroskopie in die Probe eingebracht wird, und der Länge der Kunststoffrohre, die durch die Temperatur im Labor beeinflusst werden kann, wurde die Messtemperatur jedoch bei 10 °C gehalten. Als Referenzprobe wurde UHT-Milch mit 1,5 % Fett verwendet. Milch mit verschiedenen Veränderungen wie unterschiedlichen Fettgehalten, verschiedenen Messtemperaturen und Zusatz von Wasser oder Reinigungslösung wurde als modifizierte Probe untersucht. In dieser Untersuchung wurden nicht nur der Autoencoder, sondern auch einige chemometrische Modelle zur Erkennung von Anomalien eingesetzt. Die Hauptkomponentenanalyse (PCA) wurde untersucht, um zu prüfen, ob die verschiedenen Proben separat kategorisiert werden können. Darüber hinaus wurden zwei chemometrische Modellierungstechniken wie die Hauptkomponentenregression und die Gaußsche Prozessregression getestet, um die Fähigkeit zur Erkennung von Veränderungen zu prüfen. Mit den Ergebnissen der PCA konnte keine ausreichende Kategorisierung der verschiedenen Proben erreicht werden. Während die Hauptkomponentenregression (PCR) keine vielversprechende Vorhersage lieferte, da das zugehörige R2 bei 0,7 lag, sagte die Gaußsche Prozessregression mit einem R2 von 0,97 die Veränderungen nahezu perfekt voraus. Sowohl der trainierte Autoencoder als auch die Gaußsche Prozessregression waren in der Lage, 5 % Wasser und Reinigungslösung, einen Unterschied von 0,1 % Fettgehalt und eine Variation der Messtemperatur von 5 °C zu detektieren. Im Vergleich von Autoencoder und der Gaußschen Prozessregression ist zu erwähnen, dass die Gaußsche Prozessregression in der Lage war, mehr anormale Signale zu bestimmen als der Autoencoder, allerdings muss sie mit allen möglichen Änderungentrainiert werden. Im Gegensatz dazu muss der Autoencoder nur einmal mit Referenzsignalentrainiert und kann dann für die Online-Überwachung verwendet werden. Als letzter Teil und umzu erkennen, welche Art von Anomalien während der Milchverarbeitung auftraten, wurden verschiedene Klassifizierungsansätze wie lineare Diskriminanzanalyse, Entscheidungsbaum, Support Vector Machine und K Nearest Neighbour verwendet. Während die Entscheidungsbäume und die lineare Diskriminanzanalyse nicht in der Lage waren, die verschiedenen Arten von Anomalien effektiv zu charakterisieren, lieferten die K Nearest Neighbour und die Support Vector Machine Methode vielversprechende Ergebnisse. Die Support Vector Machine wies eine Genauigkeit von 81,4 % für den Testsatz auf, während die K Nearest Neighbour Methode eine Genauigkeit von 84,8 % ergab. Daher kann man davon ausgehen, dass beide Algorithmen in der Lage sind, verschiedene Datengruppen genau zu klassifizieren. Dies kann der Milchwirtschaft helfen, herauszufinden, was bei der Verarbeitung von Milch falsch läuft. Im Allgemeinen lieferte die Raman-Spektroskopie bessere Ergebnisse als die NIR-Spektroskopie, da die typischen Absorptionsbanden der Milchbestandteile in NIR-Spektrometern durch eine hohe Wasserabsorption in Kombination mit einer erheblichen Lichtstreuung durch Fettkügelchen beeinträchtigt werden können. Darüber hinaus war der Autoencoder als selbstlernendes System in der Lage, Veränderungen während der Milchverarbeitung korrekt zu erkennen, jedoch können Klassifizierungsalgorithmen helfen, mehr Details zu erhalten

Prediction of marketing live weights in hair goat kids using artificial neural network

In this study, marketing live weights (120th day) were predicted using artificial neural network model according to the herd, gender, birth type, maternal age, birth weight, body weight at 60th day and weaning weight (90th day) measurements of 12983 hair goat kids born between 2018-2021 years. Artificial neural networks (ANN) have been frequently used as an alternative to classical regression analysis in recent years, especially in future estimation studies in the field of livestock, and also in many different fields. In this study, it was aimed to predict the marketing weights of hair goats according to the holding, gender, birth type, maternal age, birth, 60th day and weaning weights with the ANN model. For this purpose, the multi-layer feed-forward backpropagation algorithm the ANN model, in which the number of hidden layers is one and the numbers of hidden neurons are three, was used. This model performance metrics were obtained for training set as 0.98, 0.62 and 0.55; for validation set as 0.97, 0.62 and 0.55, respectively. According to these results, it was determined that ANN can be used successfully in terms of estimation of marketing live weight in Hair goat kids. Estimating the marketing weight will enable the economic cost calculations to be obtained from kids to be evaluated both based on Turkey and on the farm basis, and to reveal future projections

Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production

Effect of introducing weather parameters on the accuracy of milk production forecast models

peer-reviewedThe objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy

ASSESSMENT OF THE TECHNICAL AND ECONOMIC POTENTIAL OF AUTOMATED ESTRUS DETECTION TECHNOLOGIES FOR DAIRY CATTLE

Poor estrus detection can limit the reproductive performance of a dairy herd. One objective of this research was to evaluate an alternative method to traditional estrus detection in the form of automated monitoring technologies. To accomplish this, the first study considered the ability of automatically monitored parameters (activity, number of steps, lying bouts, lying time, feeding time, rumination time, and temperature) to detect estrus. A second study compared automated activity monitoring to timed artificial insemination as reproductive management strategies on commercial herds. The other objective of this research was to evaluate the economic potential of automated estrus detection technologies. This was accomplished by creating and evaluating a farm specific decision support tool to determine the net present value of adopting an automated estrus detection technology

An Optimal Milk Production Model Selection and Configuration System for Dairy Cows

Milk production forecasting in the dairy industry has been an independent research topic since the early 20th century. The accurate prediction of milk yield can benefit both the processor (creameries) and the producer (dairy farmer) through developing short-term production schedules, planning long-term road maps, facilitating trade and investment in the dairy industry, improving business operations, optimising the existing infrastructure of the dairy industry, and reducing operating costs. Additionally, due to the innate characteristics of the milk production process, the accurate prediction of milk yield has been a challenging issue in the dairy industry. With the abolishment of EU milk quotas in 2015, the business requirements of milk production forecasting from the dairy industry has become increasingly important. However, to date, most of the existing modelling techniques are data dependent and each case study utilises specific data based on unique conditions. Consequently, it is difficult to compare the prediction performance of each candidate model for forecasting milk as both the data types and origins are independent from study to study. This body of work proposes an integrated forecasting framework XIX concentrating on milk production forecasting using heterogeneous input data combinations based on animal data, milk production, weather variables and other possible records that can be applied to milk yield forecasting on either the herd level or the individual cow level. The first objective of this study concerned the development of the Milk Production Forecast Optimisation System (MPFOS). The MPFOS focused on data processing, automated model configuration and optimisation, and multiple model comparisons at a global level. Multiple categories of milk yield prediction models were chosen in the model library of the MPFOS. Separated databases existed for functionality and scalability in the MPFOS, including the milk yield database, the cow description database and the weather database. With the built-in filter in MPFOS, appropriate sample herds and individual cows were filtered and processed as input datasets for different customised model simulation scenarios. The MPFOS was designed for the purpose of comparing the effectiveness of multiple milk yield prediction models and for assessing the suitability of multiple data input configurations and sources. For forecasting milk yield at the herd level, the MPFOS automatically generated the optimal configuration for each of the tested milk production forecast models and benchmarked their performance over a short (10-day), medium (30-day) and long (365-day) term prediction horizon. The MPFOS found the most accurate model for the short (the NARX model), medium and long (the surface fitting model) terms with R2 values equalling 0.98, 0.97 and 0.97 for the short, medium and long term, respectively. The statistical analysis demonstrated the effectiveness of the MPFOS as a model configuration and comparison tool. For forecasting milk yield at the individual cow level, the MPFOS was utilised to conduct two exploratory analyses on the effectiveness of adding exogenous (parity and meteorological) data to the milk production modelling XX procedure. The MPFOS evaluated the most accurate model based on the prediction horizon length and on the number of input parameters such as 1) historical parity weighting trends and 2) the utilisation of meteorological parameters. As the exploratory analysis into utilising parity data in the modelling process showed, despite varying results between two cow groups, cow parity weighting profiles had a substantial effect on the success rate of the treatments. Removal of the first lactation and applying static parity weight were shown to be the two most successful input treatments. These results highlight the importance of examining the accuracy of milk prediction models and model training strategies across multiple time horizons. While the exploratory analysis into meteorological data in the modelling process demonstrated that based on statistical analysis results, 1) the introduction of sunshine hours, precipitation and soil temperature data resulted in a minor improvement in the prediction accuracy of the models over the short, medium and long-term forecast horizons. 2) Sunshine hours was shown to have the largest impact on milk production forecast accuracy with an improvement observed in 60% and 70% of all predictions (for all test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilisation of meteorological parameters in milk production forecasting did not have a substantial impact on the overall forecast accuracy. One possible reason for this may be due to modern management techniques employed on dairy farms, reducing the impact of weather variation on feed intake and lessening the direct effect on milk production yield. The MPFOS architecture developed in this study showed to be an efficient and capable system for automatic milk production data pre-processing, model configuration and comparison of model categories over varying prediction horizons. The MPFOS has proven to be a XXI comprehensive and convenient architecture, which can perform calculations for milk yield prediction at either herd level or individual cow level, and automatically generate the output results and analysis. The MPFOS may be a useful tool for conducting exploratory analyses of incorporating other exogenous data types. In addition, the MPFOS can be extended (addition or removal of models in the model library) and modularised. Therefore the MPFOS will be a useful benchmark platform and integrated solution for future model comparisons