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

Mathematical modeling for genomic selection in Serbian dairy cattle

Mathematical modeling for genomic selection in serbian dairy cattle. - Genetika, Vol 53, No.3, 1105-1115. This manuscript has come as a result of an efficient breeding program in Serbian cattle populations for some economically important traits. Genomic selection in the last two decades has been the main challenge in animal breeding programs and genetics. Many SNP markers are used in statistical analysis in predicting the accuracy of breeding values for young animals without their performance. The new breeding tendency in the selection of young animals allows their genetic progress with reducing cost. In this study, 92 Holstein cows from various regions in Serbia were analyzed based on SNP molecular markers. Within this investigation, an empirical model was developed for the prediction of Yield Traits and Fertility Traits variables, according to Key traits data for dairy cattle. The developed model gave a reasonable fit to the data and successfully predicted Yield Traits (such as Fat and Protein Percent, Cheese Merit, Fluid Merit, and Cow Livability) and Fertility Traits variables (such as Sire Calving Ease, Heifer Conception Rate, Cow Conception Rate, Daughter Stillbirth, Sire Stillbirth, and Gestation Length). A total of 92 dairy cattle data were used to build a prediction model for the prediction of Yield Traits and Fertility Traits variables. The artificial neural network model, based on the Broyden-Fletcher-Goldfarb-Shanno iterative algorithm, showed good prediction capabilities (the r2 values during the training cycle for the before mentioned output variables were in the range between 0.444 and 0.989)

Uso de inteligência artificial com foco em visão computacional na produção de bovinos e suínos

O presente trabalho teve como objetivo realizar uma revisão sobre a história das redes neurais artificias, assim como analisar diferentes trabalhos acadêmicos de áreas com e sem relação com as ciências veterinárias, para verificar o atual estado das redes neurais no meio acadêmico. Foi determinado, por meio da análise de quarenta artigos referentes a soluções de problemas na produção animal, através do uso de redes neurais, que o maior número de trabalhos da área abrangem a espécie bovina, que o número de dados de treinamento nos trabalhos é pequeno, se comparado com bases de dados de artigos de outras áreas do conhecimento, que a maioria dos trabalhos não entram em detalhes sobre o software utilizado para implementação das redes neurais e que a maioria dos autores principais dos trabalhos atuavam em departamentos de ciência animal. Outro objetivo do trabalho foi determinar as mais eficazes arquiteturas de redes neurais, cuja tarefa compreende a predição da ocorrência de descarte de matrizes suínas provenientes de granjas de quarto sítio e unidades produtoras de leitões. Uma base de dados com 5.013 fêmeas descartadas, classificadas em sete diferentes categorias de motivos de descarte, foi utilizada para o treinamento das redes neurais. Além disso as mais eficazes arquiteturas de redes neurais, cuja tarefa compreende a predição da ocorrência de descarte de matrizes suínas provenientes de granjas de quarto sítio e unidades produtoras de leitões. Uma base de dados com 5.013 fêmeas descartadas, classificadas em sete diferentes categorias de motivos de descarte, foi utilizada para o treinamento das redes neurais. A base foi filtrada e os dados de diferentes índices produtivos das fêmeas foram utilizados para a realização de quatro experimentos. O primeiro experimento tinha o objetivo de testar a eficácia de uma rede neural em classificar o motivo de descarte das fêmeas suínas nas sete diferentes categorias. A acurácia máxima alcançada foi de 56,35%. O segundo experimento visava determinar a eficácia de uma rede neural em estimar a probabilidade de descarte das fêmeas em apenas uma categoria. A acurácia máxima alcançada foi de 99,78%. O terceiro experimento tinha como objetivo avaliar a eficácia de uma rede neural em prever as variáveis da vida produtiva do parto seguinte das fêmeas, baseada em dados dos dois partos anteriores. O erro médio absoluto mínimo alcançado foi de 1,777 para a variável de número de desmamados. O quarto e último experimento tinha o intuito de testar a capacidade de uma rede neural em determinar se uma fêmea seria ou não descartada no parto seguinte, baseada em dados de dois partos anteriores. De maneira geral, as redes neurais demonstraram adequado desempenho em encontrar padrões em diferentes dados da vida produtiva de matrizes suínas e predizer a ocorrência e a classificação de seus descartes.The objective of this work was to review the history of artificial neural networks, as well as to analyze different academic works in areas related and unrelated to the veterinary sciences, to verify the current state of neural networks in the academia. It was determined, through the analysis of forty research works referring to solutions of problems in animal production, through the use of neural networks, that the largest number of works in the area are referring to the bovine species, that the number of training data in the research works is small, compared to databases of articles from other areas of knowledge, that most of the papers do not go into detail about the software used to implement neural networks and that most of the main authors of the papers work in animal science departments. Besides that, another objective of this work was to determine the most effective neural network architectures, whose task includes the prediction of the occurrence of culling of female swine breeders from four-site units and piglet-production units. A database of 5,013 culled gilts, classified into seven different culling reason categories, was used for the neural networks training. The base was filtered and the data from different productive indexes of the females were used to perform four experiments. The first experiment aimed to test the effectiveness of a neural network in the task of classifying the reason for culling swine gilts into seven different categories. The maximum accuracy achieved was 56.35%. The second experiment aimed to determine the effectiveness of a neural network in estimating the probability of gilt culling in only one category. The maximum accuracy achieved was 99.78%. The third experiment aimed to evaluate the effectiveness of a neural network in predicting the productive life variables of the breeder's next delivery, based on data from the previous two parities. The minimum mean absolute error reached was 1.777 for the weaned number variable. The fourth and final experiment was designed to test the ability of a neural network to determine whether or not a gilt would be culled in the next parturition based on data from two previous births. In general, neural networks have shown adequate performance in finding patterns in different data of the productive life of swine breeders and predicting the occurrence and classification of their culling events

Machine learning models for predicting decisions to be made by small scale dairy farmers in Eastern Africa

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn dairy, lack of decision support tools for identifying farmers' needs and demands have caused many programs, strategies, and projects to fail. This has led to the inefficient and fragmented allocation of scarce development resources. This study demonstrated how machine learning (ML) can be used as a tool to bridge this gap; by developing ML models to be used in identifying factors that can influence farmers decisions, predicting decision to be made by a farmer and forecast on farmers demands regarding to their specific need or service. Four countries: Ethiopia, Kenya, Tanzania and Uganda were selected for this study. In the course of the study four models were developed one for each country with regard to the usage of animal supplements, keeping of exotic animals, use of Artificial insemination (AI) as breeding methods and animal milk productivity. Data was collected through face to face interviews, from a total of 16 308 small scale dairy farmers in Ethiopia (n = 4679), Kenya (n = 5278), Tanzania (3500) and Uganda (n = 2851). The decision tree algorithm was used to model categorical problems (use of supplement and breeding decision), which attained the accuracy of 78%-90%. Moreover, K-nearest neighbor was employed for numeric problems (keeping of exotic animals and animal milk productivity) with an accuracy of 0.78-0.96 Adjusted R The use of ML techniques assisted in classifying farmers based on their characteristics and it was possible to identify the key factors that can be taken then prioritized to improve the dairy sector among countries. Also, the results of this study offer a number of practical implications for the dairy industry where the proposed ML models can enable decision-makers in developing the National Dairy Master Plan and design policies that promote the growth of smallholder dairy farming. Moreover, these models shade light to potential service providers and investors who want to invest in dairy to identify potential areas or groups of farmers to focus with. 2 value

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

Just-in-time Pastureland Trait Estimation for Silage Optimization, under Limited Data Constraints

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge although typically this is assumed to be based around very large datasets. This work hypothesises that through the advantages of state-of-the-art deep learning systems, pastureland crop traits can be accurately assessed in a just-in-time fashion, based on data retrieved from an inexpensive sensor platform, under the constraint of limited amounts of labelled data. However the challenges to achieve this overall goal are great, and for applications such as just-in-time yield and moisture estimation for farm-machinery, this work must bring together systems development, knowledge of good pastureland practice, and also techniques for handling low-volume datasets in a machine learning context. Given these challenges, this thesis makes a number of contributions. The first of these is a comprehensive literature review, relating pastureland traits to ruminant nutrient requirements and exploring trait estimation methods, from contact to remote sensing methods, including details of vegetation indices and the sensors and techniques required to use them. The second major contribution is a high-level specification of a platform for collecting and labelling pastureland data. This includes the collection of four-channel Blue, Green, Red and NIR (VISNIR) images, narrowband data, height and temperature differential, using inexpensive proximal sensors and provides a basis for holistic data analysis. Physical data platforms built around this specification were created to collect and label pastureland data, involving computer scientists, agricultural, mechanical and electronic engineers, and biologists from academia and industry, working with farmers. Using the developed platform and a set of protocols for data collection, a further contribution of this work was the collection of a multi-sensor multimodal dataset for pastureland properties. This was made up of four-channel image data, height data, thermal data, Global Positioning System (GPS) and hyperspectral data, and is available and labelled with biomass (Kg/Ha) and percentage dry matter, ready for use in deep learning. However, the most notable contribution of this work was a systematic investigation of various machine learning methods applied to the collected data in order to maximise model performance under the constraints indicated above. The initial set of models focused on collected hyperspectral datasets. However, due to their relative complexity in real-time deployment, the focus was instead on models that could best leverage image data. The main body of these models centred on image processing methods and, in particular, the use of the so-called Inception Resnet and MobileNet models to predict fresh biomass and percentage dry matter, enhancing performance using data fusion, transfer learning and multi-task learning. Images were subdivided to augment the dataset, using two different patch sizes, resulting in around 10,000 small patches of size 156 x 156 pixels and around 5,000 large patches of size 240 x 240 pixels. Five-fold cross validation was used in all analysis. Prediction accuracy was compared to older mechanisms, albeit using hyperspectral data collected, with no provision made for lighting, humidity or temperature. Hyperspectral labelled data did not produce accurate results when used to calculate Normalized Difference Vegetation Index (NDVI), or to train a neural network (NN), a 1D Convolutional Neural Network (CNN) or Long Short Term Memory (LSTM) models. Potential reasons for this are discussed, including issues around the use of highly sensitive devices in uncontrolled environments. The most accurate prediction came from a multi-modal hybrid model that concatenated output from an Inception ResNet based model, run on RGB data with ImageNet pre-trained RGB weights, output from a residual network trained on NIR data, and LiDAR height data, before fully connected layers, using the small patch dataset with a minimum validation MAPE of 28.23% for fresh biomass and 11.43% for dryness. However, a very similar prediction accuracy resulted from a model that omitted NIR data, thus requiring fewer sensors and training resources, making it more sustainable. Although NIR and temperature differential data were collected and used for analysis, neither improved prediction accuracy, with the Inception ResNet model’s minimum validation MAPE rising to 39.42% when NIR data was added. When both NIR data and temperature differential were added to a multi-task learning Inception ResNet model, it yielded a minimum validation MAPE of 33.32%. As more labelled data are collected, the models can be further trained, enabling sensors on mowers to collect data and give timely trait information to farmers. This technology is also transferable to other crops. Overall, this work should provide a valuable contribution to the smart agriculture research space

Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

4.Uluslararası Öğrenciler Fen Bilimleri Kongresi Bildiriler Kitabı

Çevrimiçi ( XIII, 495 Sayfa ; 26 cm.)

National farm scale estimates of grass yield from satellite remote sensing

Globally, grasslands are an important source of food for livestock and provide additional ecosystem services such as greenhouse gas (GHG) mitigation through carbon sequestration, habitats for biodiversity, and recreational amenities. Grass is the cheapest source of fodder providing Irish farmers with an economic benefit against international competitors. Hence, to maintain profitability, farmers have to maximize the proportion of grazed grass in cow’s diet or save it as silage. The overall objective of the current research project was to build a machine-learning model to estimate grass growth nationally using earth observation imagery from the Sentinel 2 satellite constellation and ancillary meteorological data, which are known to influence grass growth. Firstly, the impact of meteorological data and Growing Degree Days (GDD) was assessed for Teagasc Moorepark experimental farm (Fermoy, Co Cork, Ireland). GDD was modified to include Soil Moisture Deficit (SMD), which included the impact of summer drought conditions in 2018. Results demonstrated the importance of GDD for grass growth estimation using ordinary linear regression (OLS). The potential evapotranspiration (PE) 0.65 (r=0.65) and evaporation (r=0.65) were equally significant variables in 2017, while in 2018 the solar radiation had the highest correlation (r=0.43), followed by potential evapotranspiration and evaporation with r of 0.42. The standard and modified GDD were equally significant variables with r of 0.65 in 2017, but both had a reduced correlation in 2018 with modified GDD (0.38, p<0.01) performing slightly better than the standard GDD (0.26, p<0.01) calculation. These models only explained 53% (RMSE of 18.90 kg DM ha-1day-1) and 36% (RMSE of 27.02 kg DM ha-1day-1) of variability in grass growth for 2017 and 2018, respectively. Considering the importance of meteorological data, an empirical grass model called the Brereton model, previously used for Irish grass growing conditions were tested. Since this model lacks a spatial element, we compared the Brereton model with the previously used machine-learning model ANFIS and Random Forest (RF) with the combination of satellite data and meteorological data for eight Teagasc farms. Overall, the machine-learning algorithms (R2= 0.32 to 0.73 and RMSE=14.65 to 24.76 kg DM ha-1day-1 for the test data) performed better than the Brereton model (range of R2=0.03 to 0.33 and RMSE=41.68 to 82.29 kg DM ha-1day-1). The RF model (with all the variables except rainfall) had the highest accuracy for predicting grass growth rate, with (R2= 0.55, RMSE = 14.65 kg DM ha-1day-1, MSE= 214.79 kg DM ha-1day-1 versus ANFIS with R2 = 0.47, RMSE = 15.95 kg DM ha-1day-1, MSE= 254.40 kg DM ha-1day-1). When developing a national model, meteorological data were missing (except precipitation). A different approach was followed, whereby the grass growing season was subdivided (January-June Agmodel 1 and July–December Agmodel 2). Phenologically, the peak grass growth in Ireland typically occurs in May, with a slow decline in subsequent months. Spring is the most important season for grassland management, where growing conditions can impact the grass supply for the whole year. The national models were developed using Sentinel 2 band metrics, spectral indices (NDVI and NDRE), and rainfall for 179 farms. Data from 2017-2019 was divided into training and testing data (70:30 split), with 2020 data used for independent validation of the final trained model. Test accuracy was higher for Agmodel 1 (R2 = 0.74, RMSE= 15.52 kg DM ha-1day-1) versus Agmodel 2 (R2 = 0.58, RMSE= 13.74 kg DM ha-1day-1). This trained model was used on validation data from 2020, and the results were similar with better performance for Agmodel1 (R2 =0.70) versus Agmodel2 (R2=0.36). The improved spatial resolution of Sentinel 2 and the availability of red-edge bands showed improved results compared with previous work based on coarse resolution satellite imagery

Electronic noses for environmental monitoring applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments’ proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures