Ganadería de precisión, una revisión a los avances dentro de la avicultura enfocados a la crianza de pollos de engorde.

Poultry production is one of the industries with the best development within the Panamanian agricultural system, however, for the next few years a growth in food demand caused by population growth is expected. This has led to solutions such as encouraging the emergence of a greater number of producers and boosting intensive animal production. Faced with this problem, a new field of research has emerged called Precision Livestock Farming (PLF), which is defined as the ability to monitor and track in real time the welfare, production, reproduction, environmental impact and health of livestock, using new technologies in artificial intelligence, automation, internet of things and information systems. This article aims to be a review on the fundamentals of precision livestock farming in broiler breeding, gathering current works and their work trends, from bibliographic research bases, with a view to the adoption of this field in future projects within Panama. As a result of this review, it was found that European countries such as Belgium, Netherlands, United Kingdom and Italy have the largest number of researchers and works related to this branch, being projects based on sensors, machine learning, artificial vision and sound analysis the current research trends, it was also found that ethical dilemmas related to animal care and welfare are still being discussed within this field.La producción avícola es una de las industrias con mejor desarrollo dentro del sistema agropecuario panameño, sin embargo, para los próximos años se espera un crecimiento en la demanda de alimentos causado por el crecimiento de la población. Esto ha planteado soluciones como fomentar la aparición de un mayor número de productores y potenciar la producción animal intensiva, ante esta problemática ha surgido un nuevo campo de investigación denominado Ganadería de precisión (PLF), este es definido como la capacidad de monitorizar y de dar seguimiento en tiempo real al bienestar, producción, reproducción, impacto ambiental y salud del ganado, empleando nuevas tecnologías en Inteligencia artificial, automatización, internet de las cosas y sistemas de información. Este artículo tiene por objetivo ser una revisión sobre los fundamentos de la ganadería de precisión en la crianza de pollos de engorde, reuniendo trabajos de actualidad y sus tendencias de trabajo, desde bases de investigación bibliográficas, con miras a la adopción de este campo en los proyectos futuros dentro de Panamá. Como resultado de esta revisión se encontró que los países europeos como Bélgica, Países Bajos, Reino Unido e Italia tienen la mayor cantidad de investigadores y trabajos relacionados con esta rama, siendo proyectos basados en sensores, machine learning, visión artificial y análisis del sonido las actuales tendencias de investigación, también se encontró que aún se discuten dilemas éticos relacionados con el cuidado y bienestar animal dentro de este campo

An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification

This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7

An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification

This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7

The potential of non-movement behavior observation method for detection of sick broiler chickens

The poultry industry, which produces excellent sources of protein, suffers enormous economic damage from diseases. To solve this problem, research is being conducted on the early detection of infection according to the behavioral characteristics of poultry. The purpose of this study was to evaluate the potential of a non-movement behavior observation method to detect sick chickens. Forty 1-day-old Ross 308 males were used in the experiments, and an isolator equipped with an Internet Protocol (IP) camera was fabricated for observation. The chickens were inoculated with Salmonella enterica serovar Gallinarum A18-GCVP-014, the causative agent of fowl typhoid (FT), at 14 days of age, which is a vulnerable period for FT infection. The chickens were continuously observed with an IP camera for 2 weeks after inoculation, chickens that did not move for more than 30 minutes were detected and marked according to the algorithm. FT infection was confirmed based on clinical symptoms, analysis of cardiac, spleen and liver lesion scores, pathogen re-isolation, and serological analysis. As a result, clinical symptoms were first observed four days after inoculation, and dead chickens were observed on day six. Eleven days after inoculation, the number of clinical symptoms gradually decreased, indicating a state of recovery. For lesion scores, dead chickens scored 3.57 and live chickens scored 2.38. Pathogens were re-isolated in 37 out of 40 chickens, and hemagglutination test was positive in seven out of 26 chickens. The IP camera applied with the algorithm detected about 83% of the chickens that died in advance through non-movement behavior observation. Therefore, observation of non-movement behavior is one of the ways to detect infected chickens in advance, and it appears to have potential for the development of remote broiler management system

Precision Poultry Farming

This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg productio

Precision livestock farming towards broiler welfare

Due to intensification of the livestock system the ratio between number of broilers and number of farmers have been increasing, making impossible the individualized attention to animals without the use of appropriate tools. Increasingly societal concern on broiler welfare requires farmers to find means to improve animal welfare level. Precision livestock farming (PLF) emerges as a possible solution as it enables the monitoring of animals and its environment 24/7. The present study aims to provide information on how PLF technologies can address broiler welfare and to evaluate reasons for their adoption (or non-adoption) by farmers. The results discussions and analysis are based in the three main pillars that guide the present research: animal welfare, PLF technologies and innovation adoption. Methodologically, the study consists of two different steps. Initially, a systematic review of the literature was carried out to identify which are the PLF technologies related to broiler welfare and to assess how they address birds ́ welfare. Results indicate that most PLF technologies are related to image analysis and mainly focused on broiler health improvements. In the second stage, an empirical research was carried out with broiler farmers in the Southern Brazil. From this survey, information on broiler farmers ́ opinions towards broiler welfare and PLF potentialities were assessed as well as on the determinants and limiting factors for technologies adoption. In general, Brazilian broiler farmers attribute great importance to broiler welfare and perceive the current level of welfare as high; however higher scores for importance than for perception indicate that there is room for welfare improvements. In broiler farmers ́ opinions, providing animals food/water and good housing and health conditions are more important than provide means for the animals to express their natural behaviors. Broiler farmers believe that technologies can help them on welfare improvements and are willing to adopt them even when no extra income come from this. Broiler farmers with less experience, producing chicken grillers, having other farm activity besides broiler production and presenting high beliefs on PLF potentialities regarding animal welfare improvements are more likely to adopt PLF technologies. Major limiting factors for PLF technologies adoption are regarding technology high prices, maintenance requirements and to possible financial consequences with technical problems. It is expected the present thesis to be useful to clarify about PLF technologies opportunities in the broiler farmers point of view and that the results obtained to be valuable to increase PLF adoption, which can potentially improve animal and farmers welfare alike.A intensificação do sistema produtivo aumentou a relação entre o número de frangos de corte e o número de trabalhadores rurais, impossibilitando a atenção individualizada aos animais sem o uso de ferramentas adequadas. Em paralelo, a sociedade pressiona os produtores a encontrarem meios para aumentar o nível bem-estar animal (BEA). Tecnologias da zootecnia de precisão (ZP)surgem como possívelsolução, pois possibilitam o monitoramento dos animais e de seu ambiente de forma contínua. O presente estudo objetiva fornecer informações sobre como as tecnologias da ZP abordam o bem-estar de frangos de corte e avaliar os fatores que influenciam a sua adoção pelos produtores. A discussão e a análise dos resultados baseiam-se em três pilares, a saber: BEA, tecnologias da ZP e adoção de inovações. Metodologicamente, o estudo é composto por duas etapas distintas. Inicialmente, uma revisão sistemática da literatura foi realizada para identificar quais são as tecnologias da ZP relacionadas ao bem-estar de frangos de corte e para avaliar como elas abordam o bem-estar das aves. Os resultados indicam que a maioria das tecnologias está relacionada à análise de imagens e principalmente focada na melhoria da saúde dos frangos. Na segunda etapa, foi realizada uma pesquisa empírica com produtores de frangos de corte no Sul do Brasil. A partir desta pesquisa, foram avaliadas informações sobre as opiniões dos criadores de frangos de corte em relação ao BEA e às potencialidades das tecnologias, bem como sobre os fatores determinantes e limitantes para adoção de tecnologias. Em geral, os avicultores brasileiros atribuem grande importância ao bem-estar dos frangos e consideram alto o nível atual de BEA; no entanto, maiores escores para importância do que para percepção indicam que há espaço para melhorias. Na opinião dos produtores, fornecer aos animais comida/água e boas condições de alojamento e saúde é mais importante do que fornecer meios para que os animais expressem seus comportamentos naturais. Os produtores acreditam que as tecnologias podem ajudá-los a aumentar o BEA e estão dispostos a adotá-las mesmo que isso não resulte em maior renda. Produtores com menos experiência, que produzem grillers, que possuem mais de uma atividade agropecuária e que acreditam nas potencialidades das tecnologias em melhorar o BEA são mais propensos a adotar tecnologias. Os principais fatores limitantes para a adoção de tecnologias são os preços elevados, as exigências de manutenção e as possíveis consequências financeiras com problemas técnicos. Espera-se que a presente tese seja útil para esclarecer sobre as oportunidades da ZP do ponto de vista dos produtores e que os resultados obtidos sejam valiosos para aumentar a adoção de tecnologias, as quais podem melhorar o BEA e o bem-estar dos produtores

Developing and applying precision animal farming tools for poultry behavior monitoring

Appropriate measurement of broiler behaviors is critical to optimize broiler production efficiency and improve precision management strategies. However, performance of different precision tools on measuring broiler behaviors of interest remains unclear. This dissertation systematically developed and evaluated radio frequency identification (RFID) system, image processing, and deep learning for automatically detecting and analyzing broiler behaviors. Then different behaviors (i.e., feeding, drinking, stretching, restricted feeding) of broilers under representative management practices were measured using the developed precision tools. The broilers were Ross 708 in weeks 4-8. The major findings show that the RFID system achieved high performance (over 90% accuracy) for continuously tracking feeding and drinking behaviors of individual broilers, after they were customized and modified, such as tag sensitivity test, power adjustment, radio wave shielding, and assessment of interference by add-ons. The image processing algorithms combined with a machine learning model were customized and adjusted based on the experimental conditions and finally achieved 85% sensitivity, specificity, and accuracy for detecting bird number at feeder and at drinkers. After adjusting labeling method and hyperparameter tuning, the faster region-based convolutional neural network (faster R-CNN) had over 86% precision, recall, specificity, and accuracy for detecting broiler stretching behaviors. In comprehensive algorithms, the faster R-CNN showed over 92% precision, recall, and F1 score for detecting feeder, eating birds, and birds around feeder. The bird trackers had a 3.2% error rate to track individual birds around feeder. The support vector machine behavior classifier achieved over 92% performance for classifying walking birds. Image processing model was also developed to detect birds that were restricted to feeder access. Broilers had different behavior responses to different sessions of a day, bird ages, environments, diets, and allocated resources. Reducing stocking density, increasing feeder space, and applying poultry-specific light spectrum and intensity were beneficial for birds to perform behaviors, such as feeding, drinking, and stretching, while using the antibiotics-free diet reduced bird feeding time. In conclusion, the developed tools are useful tools for automated broiler behavior monitoring and the measured behavior responses provide insights into precision management of welfare-oriented broiler production