Computer vision algorithms as a modern tool for behavioural analysis in dairy cattle

Looking at modern dairy production, loose housing, i.e. free stalls became one of the most common practices, which, while widely implemented along with different management routines, do not always include the adjustments necessary for assuring animal welfare. The analysis of interactions occurring between cows in dairy barns and their effect on health and performance is of great importance for sustainable, animal-friendly production. The general aim of this thesis was to investigate the possibilities and limitations of computer vision approach for studying dairy cattle behaviour and interactions between animals, as well as take a first step towards the fully automated system for continuous surveillance in modern dairy barns. In the first study, a seven-point shape-model for describing a cow from the mathematical perspective was proposed and investigated. A pilot study showed that the proposed Behavioural Detector based on the developed shape-model provided a solid basis for behavioural studies in a real-life dairy barn environment. The second study investigated a classification case from the industry: how animal distribution and claw positioning in specific areas could affect the maximal load on floor elements. The results of the study provided more substantial background data for determining the dimensioning of the strength of the slats. The third study aimed to take the first step towards an automated system (so-called WatchDog) for behavioural analysis and automatic filtering of the recorded video material. The results showed that the proposed solution is capable of detecting potentially interesting scenes in video-material with the precision of 92,8%. In the fourth and final study, a state-of-the-art tracking/identification algorithm for multiple objects with near-real-time implementation in crowded scenes with varying illumination was developed and evaluated. The algorithms forming the multi-modular WatchDog system and developed during this project are the crucial stepping stone towards a fully-automated solution for continuous surveillance of health and welfare-related parameters in dairy cattle. The proposed system could also serve as evaluation/benchmark tool for modern dairy barn assessment. Keywords: dairy cattle, image analysis, Precision Livestock Farming, computer vision, deep learning, convolutional neural networks, social interactions, tracking, cow traffi

Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting

With increasing herd sizes came an enhanced requirement for automated systems to support the farmers in the monitoring of the health and welfare status of their livestock. Cattle are a highly sociable species, and the herd structure has important impact on the animal welfare. As the behaviour of the animals and their social interactions can be influenced by the presence of a human observer, a camera based system that automatically detects the animals would be beneficial to analyse dairy cattle herd activity. In the present study, eight surveillance cameras were mounted above the barn area of a group of thirty-six lactating Holstein Friesian dairy cows at the Chamber of Agriculture in Futterkamp in Northern Germany. With Mask R-CNN, a state-of-the-art model of convolutional neural networks was trained to determine pixel level segmentation masks for the cows in the video material. The model was pre-trained on the Microsoft common objects in the context data set, and transfer learning was carried out on annotated image material from the recordings as training data set. In addition, the relationship between the size of the used training data set and the performance on the model after transfer learning was analysed. The trained model achieved averaged precision (Intersection over union, IOU = 0.5) 91% and 85% for the detection of bounding boxes and segmentation masks of the cows, respectively, thereby laying a solid technical basis for an automated analysis of herd activity and the use of resources in loose-housing

Estimating conformational traits in dairy cattle with deepAPS : A two-step deep learning automated phenotyping and segmentation approach

Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image. This method performs accurately when compared with manual classification of proportion of coat color with an adjusted R2 = 0.926. Using the output mask, we are able to automatically extract useful phenotypic information for 14 additional morphological features. Using pedigree and image information from a web catalog (www.semex.com), we estimated high heritabilities (ranging from h2 = 0.18-0.82), indicating that meaningful biological information has been extracted automatically from imaging data. This method can be applied to other datasets and requires only a minimal number of image annotations (50) to train this partially supervised machinelearning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https://github.com/lauzingaretti/deepaps

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Simple SummaryMonitoring the welfare of cattle and sheep in large pastures can be time-consuming, especially if the animals are scattered over large areas in semi-natural pastures. There are several technologies for monitoring animals with wearable or remote equipment for recording physiological or behavioural parameters and trigger alarms when the acquired information deviates from the normal. Automatic equipment allows continuous monitoring and may give more information than manual monitoring. Ear tags with electronic identification can detect visits to specific points. Collars with positioning (GPS) units can assess the animals' movements and habitat selection and, to some extent, their health and welfare. Digitally determined virtual fences, instead of the traditional physical ones, have the potential to keep livestock within a predefined area using audio signals in combination with weak electric shocks, although some individuals may have difficulties in responding as intended, potentially resulting in reduced animal welfare. Remote technology such as drones equipped with cameras can be used to count animals, determine their position and study their behaviour. Drones can also herd and move animals. However, the knowledge of the potential effects on animal welfare of digital technology for monitoring and managing grazing livestock is limited, especially regarding drones and virtual fences.The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences

Assessment of individual- and group-level behavioral variation in dairy cattle: from personality to social networks

Currently little consideration is given to individuality and social behavior when managing dairy cow groups. This thesis assessed three objectives: 1) Investigating personality in adult lactating cows, 2) comprehensive analysis of social networks in a free-stall barn, 3) facilitating the automated assessment of agonistic behavior. Findings suggest that the level of individuals and the level of social groups should also be taken into account when assessing dairy cattle welfare. The presented automatic method for detecting agonistic behavior and dominance is one important step in this direction

How is time budget, milk yield and feed intake in dairy cows affected by the time spent in the waiting area in AMS?

Automatic milking systems (AMS) are used in many countries worldwide, and it is an efficient system with several benefits for both the farmer and the dairy cows. The cows can move freely in the barn and they therefore decide to a large extent over their own time budget throughout the day. The cows are kept in loose housing systems and the cow traffic in the barn has an important role in the production. The milking frequency in AMS usually increases compared with conventional morning and evening milking systems and thus the milk yield can also be higher in AMS. In guided cow traffic systems, there is a waiting area in front of the milking robot that only cows with milking permissions have access to. In the waiting area, the cows wait to be milked and are then released to the feeding area or to the lying area depending on cow traffic system. Studies have shown that some cows, mostly animals with a low social rank spend a long time in the waiting area which may have a negative impact on cow welfare, and it can also lead to a decline in production. This study, therefore, aims at investigating whether time in the waiting area affects the time budget of the cows’ feed intake and milk yield. The study included a literature review, analysis of existing data that had been collected in the AMS unit at the Swedish livestock research centre, Lövsta SLU Uppsala and a behavior study in the same unit but at a later time. The behaviour study included 54 dairy cows, of the Swedish Red Breed (SRB) and Holstein. The study intended to determine the cows’ time budget in order to evaluate effects of longer or shorter waiting time on cow activities. The result of the study showed that milking intervals increased by 0.3 minutes for each minute the cows were in the waiting area. Milk yield also increased with increased waiting time by 0.026 kg for every minute. There was no difference between cows in first parity and older cows or between the breeds in milking interval or milk yield. Feed intake was not affected by the time in the waiting area and there was no difference between the breeds in the existing data being analyzed. On the other hand, the roughage intake differed significantly for both breed and lactation numbers in the behavioral study. Holstein cows consumed 2.5 kg DM more per day than SRB cows. Older cows consumed 8.7 kg DM more than first parity cows per day. In the time budget, there was a difference between young and older cows, where the first parity cows showed on average 98 minutes longer standing time in the system than older cows each day. Older cows ruminated 79 minutes longer a day compared to first parity cows, 581 minutes and 502 minutes, respectively. The behavioral study showed that first parity cows waited an average of 238 minutes and older cows waited 115 minutes per day in the waiting area. Data from the milking robot also showed a significant difference between lactations but with shorter waiting times, 163 minutes for first parity cows and 95 minutes for older cows. This data also showed a significant difference between the breeds where SRB cows waited longer time than Holstein cows, 156 minutes and 102 minutes, respectively.Idag används automatiska mjölkningssystem (AMS) i många länder världen över, det är ett effektivt system med flera fördelar både för lantbrukaren och mjölkkorna. Korna hålls i lösdrift och kotrafiken i stallet har en betydande roll för produktionen. Eftersom korna inte står uppbundna utan kan röra sig fritt i stallet bestämmer de till stor del över sin egen tidsbudget över dagen. Vanligen ökar mjölkningsfrekvensen i AMS jämfört med konventionella system med morgon- och kvällsmjölkning och därmed kan även en ökning i mjölkavkastning ses. I styrda kotrafiksystem finns en väntfålla framför mjölkningsroboten som endast kor med mjölkningstillstånd har tillgång till. I väntfållan väntar korna på sin tur att bli mjölkade och blir sedan slussade till foderavdelningen eller liggavdelningen beroende på vilket kortrafikssystem gården har. Studier har visat att vissa kor spenderar lång tid i väntfållan och att det kan påverka deras välfärd negativt vilket också kan leda till en försämrad produktion. Denna studie syftar därför till att undersöka om tiden i väntfållan påverkar mjölkkornas tidsbudget, foderintag och mjölkavkastning. Studien är uppdelad i en litteraturstudie, en analys av redan befintliga data och en beteendestudie. Beteendestudien utfördes under en vecka i november 2017 i AMS-avdelningen, nötstallet vid Lövsta lantbruksforskning, SLU Uppsala, där styrd kotrafik med selektionsgrindar ”foder först principen” användes. I studien ingick 54 mjölkkor, både svensk röd och vit boskap (SRB) och Holsteinkor. Studiens syfte var att kartlägga mjölkkornas tidsbudget för att kunna fastställa om beteendemönstret skilde sig mellan kor som spenderade längre eller kortare tid i väntfållan och om det fanns ett mönster för vilka kor som spenderade längst tid i väntfållan och vad det i så fall berodde på. Resultatet av studien visade att mjölkningsintervallen ökade med 0,3 minuter för varje minut korna stod i väntfållan. Mjölkavkastningen ökade med 0,026 kg för varje minut i väntfållan. Det fanns ingen skillnad mellan förstakalvare eller äldre kor eller mellan raserna i varken mjölkningsintervall eller mjölkmängd. Foderintaget påverkades inte av tiden i väntfållan och det fanns ingen skillnad mellan raserna i den befintliga data som analyserades. Däremot skilde sig ensilageintaget signifikant för både ras och laktationsnummer i beteendestudien. Holsteinkor åt i genomsnitt 2,5 kg ts mer per dag än SRB kor. Äldre kor åt i genomsnitt 8,7 kg ts mer än förstakalvare om dagen. Beteendestudien visade skillnader mellan laktationsnummer där äldre kor idisslade 79 minuter längre jämfört med förstakalvare, 581 minuter respektive 502 minuter per dag. Förstakalvare stod upp 98 minuter längre tid jämfört med äldre kor, 206 minuter respektive 108 minuter dagligen. Beteendestudien visade att förstakalvare väntade i genomsnitt 238 minuter och äldre kor väntade 115 minuter per dag i väntfållan. Mjölkningsrobotens data visade också en signifikant skillnad mellan laktationsnummer men med kortare väntetider, 163 minuter för förstakalvare och 95 minuter för äldre kor. Denna data visade även en signifikant skillnad mellan raserna där SRB väntade i genomsnitt längre än Holstein, 156 minuter jämfört med 102 minuter

Labour organisation on robotic milking dairy farms

1. Research issuesThe research described in this dissertation is focused on the effects of the integration of the milking robot in a dairy farm on the labour organisation at operational and tactical level. Attention was paid to the future requirements concerning human labour and labour (re)organisation with respect to the complex interaction between the cows and an automatic milking system (AMS) on a robotic milking dairy farm. The study was divided in a number of research issues (Chapter 1) :(a) What is the capacity of a milking robot ? In determining the amount of human labour that can be replaced by the milking robot, the capacity of the milking robot forms an indispensable basis for calculations of possible labour requirement when an AMS is used.(b) What are the remaining "milking" operations and work elements of the farmer according to the chosen work method with the AMS ? Because cows will be kept closer to the milking system, other grazing systems than unrestricted grazing where cows are pasturing day and night will have to be applied with automatic milking. Therefore, the question requires an evaluation of the work methods with an AMS and grazing systems. A grazing system refers here to a specific time distribution of "keeping cows in- or outdoors" on daily and yearly basis.(c) What are the effects of different AMS management strategies on the daily labour requirement and labour organisation at operational level?(d) What are the effects of different AMS management strategies on the annual labour requirement and the labour organisation at tactical level ? What are the possible annual labour savings in comparison with conventional milking dairy farms?(e) The results of the above-mentioned research questions will have to give indications about labour quality and the quality of life of the farmer on robotic milking dairy farms.The two main AMS management strategies considered in the dissertation are : (1) the fullyautomatic strategy with computer-controlled cow traffic based on voluntary visits of the cows to the milking point during 24 h of the day, a cubicle house with restricted one-way cow traffic and individual concentrate supplementation and milking (AM-CCT) and (2) a semi-automatic strategy with humancontrolled cow traffic based on milkings at fixed moments of the day, under supervision of the farmer and either individual concentrate supplementation by means of computerized self-feeders or group feeding using a total mixed ration (AM-HCT). All studies discussed in this dissertation used an automatic milking system with a 'Prolion' milking robot with one robot arm serving one or more milking stalls. From an evaluation of the dairy technology, we conclude that technical solutions for the automation of each milking operation performed in conventional milking parlours are potentially available (Chapter 2). Automatic milking can replace the milker and the milking parlour to a large extent. 2. The milking capacity of a milking robotThe interaction between milking robot and cow was investigated by means of a simple formula of tuning which enables to calculate the milking capacity of any type of milking robot (Chapter 3 or research issue (a)). Using this static model, the main factors of robotic milking such as the times required for cow movements, milking processes and robot motions were investigated. It was shown that for a milking robot with one robot arm serving two stalls, the idle time of the robot arm was 54% and that the robot arm can serve up to four milking stalls in-line. The model showed also that the capacity of an AMS arrangement with two stalls in-line, can be increased from 11.7 to nearly 15.4 cows/h by increasing the robot speed, by simultaneous executing of some milking processes such as the simultaneous opening of the entrance doors of the milking stall and the milking parlour, and by changing the sequence of milking processes. If the robot is available for milking for 20 h, 308 milkings could theoretically be carried out. Thus, with a milking frequency of four milkings per cow per 24 h for the whole herd, the milking robot could serve nearly 80 cows.With the same modelling approach, formulae were developed to assess and evaluate fictitious AMS arrangements and to estimate capacities. A double or rotary tandem AMS arrangement can be a good alternative for an AMS with four or five milking stalls in-line. In a 2 x 2 tandem arrangement the capacity (cows/h) would be 8.5% higher than in a one-row arrangement. Cycle analysis showed that with a rotary tandem comprising five milking stalls a capacity of 29 cows/h can be reached. Arrangements with more than five milking stalls do not improve capacity, if the speed of the robot processes cannot be increased. If these processes could be carried out faster six milking stalls could be used and a capacity of nearly 39 cows/h could be reached.3. Labour organisation at operational levelIn general the farmer can allocate his time to milking job, non-milking jobs, personal care and social activities. Two kinds of operations can be distinguished in the milking job using automatic milking viz. planned and unplanned milking operations.Times for planned milking operations were derived from observations on commercial farms where automatic milking was combined with a human-controlled cow traffic and on an experimental farm where automatic milking was combined with computer-controlled cow traffic (Chapter 4 or research issue (b)). Based on these work studies, the 'planned' milking operations of the milking job were derived for automatic milking methods combined with five grassland strategies. Seventeen variants were quantified by means of a case-study. Calculations with a developed task time program show that the automatic milking method with human-controlled cow traffic applied during the whole year and with a milking frequency of three times a day results in important physical labour savings for milking (37.9%). This method allows to apply grazing systems where cows are pasturing even day and night. However, automatic milking with computer-controlled cow traffic with cows kept indoors the whole year results in the largest labour reduction (66.1%).The unplanned milking operations include (1) repair of robot failures, (2) bringing cows that exceed a maximum milking interval to the milking point and (3) interventions for cows which fail automatic teatcup attachment. Malfunctioning of the AMS will determine the occurrence of unplanned milking operations which can disturb the daily labour planning. On the other hand, the daily planned tasks during which the farmer cannot be disturbed and is unavailable to the AMS, will delay unplanned milking operations and therefore negatively affect AMS functioning. To study the interdependency of automatic milking and labour planning at operational level, a dynamic stochastic simulation model (Chapter 5) and a program for labour planning quality (Chapter 6) were developed.For bringing cows to the AMS, it was found that bringing cows during three fixed periods to the AMS is preferable with respect to the low labour requirement, the low impact on labour planning and the negligible negative effect on the average milking interval and the milk production. The farmer will have to learn how the cows behave in the cubicle house and depending on their visiting pattern and production level he has to choose the maximum allowable milking interval. The choice of the maximum milking interval has a marked influence on the number of cows that need to be brought to the AMS and consequently, on the labour requirement of this operation.One of the most important concerns of potential robotic milking dairy farmers is how to deal with robot failures. Robot failures and repair are defined as unplanned milking operations. The simulation model described in Chapter 5 makes it possible to study the effects of robot failures on the quality of the milking process for different degrees of availability of the farmer or the maintenance service to the system. The results show that a permanently available maintenance service is very important to guarantee the quality of the milking process, especially on those farms where the AMS already operates at the limit of its capacity. We learned that the milking process will benefit more from a farmer who is able to repair most of the robot failures himself and without delay than from one who always immediately calls in the maintenance service of the robot manufacturer.Cows unsuitable for automatic teatcup attachment require additional work from the farmer. The farmer will have to set a maximum time that he wants to spend for this operation. When the farmer aims to spend a maximum of 0.5 h per group (three groups per day) for milking the separated cows, the herd may consist of 6 to 7% cows that are unsuitable for automatic attachment. Culling of these cows can be considered to reduce the labour input. If the cow is a high producing one, the decision may be hard. It is up to the fanner to set out the pros and cons before taking a decision. Failing teatcup attachment can also be caused by the system itself. We can derive that the milking robot has to achieve an attachment score of 93 to 94% to limit the additional work of the farmer to 1.5 h a day. This additional work consists mainly of supervision. Only 12 to 20% of the time is used for physical work.4. Labour organisation at tactical levelThe effects of the integration of an automatic milking system on the labour organisation of a dairy farm at tactical level will depend on the characteristics of the farm at the moment of the introduction of the AMS and on the automatic milking management strategy applied once the AMS is integrated in the farm. By combining two existing programs, namely the IMAG-ARBGRO labour budgeting program, extended with task time modules for automatic milking and the program 'Standards for Fodder Supply', it was possible to calculate the labour budget of robotic milking dairy farms with various grazing systems applied and to compare these farms with conventional milking dairy farms (Chapter 7). In several experiments we studied the following grazing systems combined with automatic milking : unrestricted grazing with or without supplementary feeding of maize silage, restricted grazing with or without supplementary feeding of maize silage, zerograzing (cows indoors the whole year and feeding fresh cut grass) and summerfeeding (cows indoors the whole year and feeding grass or maize silage). Fully automatic milking based on a 24 hours attendance of cows to the AMS supposes summerfeeding or zerograzing.Comparing various grazing systems for farming plans with grassland only we found that all cases result in a labour reduction of at least 15%, with a maximum of 22.2% (approx. 1150 h) for restricted grazing or overnight housing with supplementary feeding of 6 kg DM maize silage per day. For farming plans with grass- and maizeland the introduction of an AMS results in labour savings of 800 to 1000 h per year for all grazing systems compared. Farmers using contract workers for grass silage production will profit more from automatic milking in terms of labour reduction. For these farms, the labour reduction ranges from 923 h (20.0%) to 1371 h (29.6%) for farming plans with only grassland and 816 h (17.9%) to 1361 h (29.9%) for farming plans with grassland and maizeland. If we compare different grazing systems for this case, summerfeeding appears to be the best alternative.From the discussion in Chapter 8, we summarize that automatic milking is a working tool for the farmer that will lighten the mental and physical load and as such will lead to a higher work quality. The farmer will need to become acquainted with this technically and electronically sophisticated device. The farmer will become more a brain worker than a manual labourer. The effects of automatic milking on his family and social life will depend on the AMS management strategy chosen by the farmer. Some cases where automatic milking may lead to a higher stress are discussed. In this context, labour psychological studies are needed to learn how the farmer and his family deal with stress situations and how they solve the related problems.5. Final conclusions- With a simple formula for tuning for robot and cow cycle duration it is possible to show that the capacity of current automatic milking systems can be improved by changing the sequence of certain activities and by programming the simultaneous execution of activities, like the simultaneous opening of the entrance doors of the milking stall and the milking parlour. These adjustments result in shorter idle times for the cows and the robot arm and consequently in shorter milking times.- Automatic milking with human-controlled cow traffic is a suitable way of milking. In comparison to conventional milking AM-HCT applied throughout the year and with a milking frequency of three times a day results in marked labour savings for the milking job (37.9%), The AM-HCT method can only be applied on farms with a small herd size (- Automatic milking with computer-controlled cow traffic merely requires starting-up procedures, cleaning tasks and a regular inspection during the day. The AM-CCT method results in a labour reduction of 66.1% for the milking job in comparison to conventional milking. Unexpected failures or repairs were not included in these calculations.- The AMS management strategy will determine the absolute and relative importance of the labour requirement for planned and unplanned milking operations. With regard to the amount of labour for the different AMS management strategies under unfavourable circumstances, unplanned milking operations lead to marked reductions in labour savings with the AMS. Therefore, high demands have to be set to AMS functioning and to cow traffic to the AMS.- A permanently available maintenance service is very important to assure the quality of the milking process, especially on those farms where the AMS already operates at the limit of its capacity. The results indicate that a training of the farmer in which he is taught the basics to repair small robot failures is a worthwhile investment.- For labour organisation at tactical level when using an AMS, we stress that the labour requirement and labour savings will largely depend on the decisions taken by the farmer with respect to the use of contract work, the use of the available farmland and on the milk quota. In all experiments, the labour budget of a robotic milking dairy farm results in labour savings when compared to conventional milking dairy farms. We found labour savings of minimum 1.8% (91 h) for a farming plan with only grassland, summerfeeding and a herd of 80 cows and labour savings of maximum 29.9% (1361 h) for a farming plan with grassland and maizeland, summerfeeding a herd of 70 cows and with contract work for grass and maize silage production.- Family farms with up to 80 cows will benefit most of fully automatic milking in terms of labour reduction, especially when summerfeeding is applied and contract workers are hired for grass silage production. It will result in a low labour input throughout the year (slight labour peaks). Other grazing systems will result in more work for the milking job and lower labour savings. When fully automatic milking is applied on large farms, the herd will have to be divided into small groups (40 to 80 cows). summerfeeding can then be applied with or without the employment of contract workers for grass silage production. The size of the farmland will here determine which solution will be the most economical one. If one wants to apply a grazing system in which cows are pasturing, the AMS will need to have a high capacity in order to apply automatic milking with a human-controlled cow traffic. The grazing system of the conventional milking dairy farm can be continued.- Automatic milking will contribute to a lower physical and mental load of the farmer and his relatives if problems with cow traffic and technical problems can be kept to a minimum. The farmer will become more an intellectual worker than a manual labourer. More time will be available for animal care and farm management in general. Automatic milking can improve the farmer's social and family life. For certain persons automatic milking might lead to task enlargement and task enrichment, for others however, it might lead to stress situations. Therefore, a labour psychological study to investigate objectively the negative and positive psychological consequences of the robotization for the (potential) robotic milking dairy farmers and their family is recommended

Metodologías de Ganadería de Precisión y su Aplicación en Colombia, Revisión Sistemática

La Ganadería de Precisión está relacionada con la implementación de tecnologías de medición en línea, comunicaciones y la implantación de buenas prácticas empresariales relacionadas con la gestión y análisis de información (Data Mining); tiene como fin esencial maximizar los ingresos, velar por la conservación, el bienestar y la salud del campo y los animales, esto se consigue garantizando un proceso de producción estable y continuo a partir de la implementación de sistemas de telemetría que permiten conocer oportunamente si algún animal podría entrar en estado no productivo..

A GP approach for precision farming

Abbona, F., Vanneschi, L., Bona, M., & Giacobini, M. (2020). A GP approach for precision farming. In 2020 IEEE Congress on Evolutionary Computation, CEC : 2020 Conference Proceedings (pp. 1-8). [9185637] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185637Livestock is increasingly treated not just as food containers, but as animals that can be susceptible to stress and diseases, affecting, therefore, the production of offspring and the performance of the farm. The breeder needs a simple and useful tool to make the best decisions for his farm, as well as being able to objectively check whether the choices and investments made have improved or worsened its performance. The amount of data is huge but often dispersive: it is therefore essential to provide the farmer with a clear and comprehensible solution, that represents an additional investment. This research proposes a genetic programming approach to predict the yearly number of weaned calves per cow of a farm, namely the measure of its performance. To investigate the efficiency of genetic programming in such a problem, a dataset composed by observations on representative Piedmontese breedings was used. The results show that the algorithm is appropriate, and can perform an implicit feature selection, highlighting important variables and leading to simple and interpretable models.authorsversionpublishe