My Farm

Being among the companies offering solutions for artificial intelligence applications in farms makes us proud as one of the few companies worldwide, whose numbers can be counted on the fingers of two hands. Especially, the fact that these software are completely original, domestic, and national further enhances this pride.

We see artificial intelligence applications, which we call the "KIZILELMA" (Red Apple) of our livestock, as one of the pioneering examples of national agriculture. Regardless of which livestock sector you are involved in (cattle, small ruminants, or poultry), these applications will undoubtedly become a part of your life one day.

In cattle farms, especially using the latest smart drone technology, when your animals enter the feed lane or are in any part of the barn, the drone automatically takes off from the charging station at preset intervals, approaches the paddocks, and identifies each animal, capturing detailed video footage. It records individual videos of your animals' external appearances, their body temperatures, udder temperatures, vulva temperatures, and appearances into the database. If there is any issue, it immediately notifies you.

This way, you can instantly learn how close the animal is to giving birth without being near the animal. If you wish, it attaches a short video and photos of the problematic animal and the affected area to the notification.

The first phase of this advanced artificial intelligence platform, which we call the "CEO of the Farm," has been made available to farm owners under the name "KAHYA Artificial Intelligence Farm Animal Counting Program," as a product of approximately two years of work.

The KAHYA Artificial Intelligence Farm Animal Counting Program performs a complete count of your farm animals going out to or returning from the pasture using a video camera. This count can be done live as the animals pass by or from a previously recorded video. If you wish, the counting result can also be sent via SMS to 5 designated phone numbers. A very special camera is not required for video recording; an HD camera is sufficient. Security cameras currently available in the market will suffice for the counting task. It is possible for artificial intelligence to perform 100% accurate counting, but some adjustments may be needed. You can learn what these adjustments are from our experts when you contact us.

The second phase, aimed at tracking the estrus of your animals, along with monitoring their health and welfare, is still ongoing. While monitoring your animals, the system will also provide outputs according to Welfare Quality criteria, allowing you to examine your animals' happiness and health levels. In the near future, you will be able to relax by leaving critical processes such as estrus and health tracking to the reliable hands of artificial intelligence.

The third phase of the "CEO of the Farm" aims to determine the individual feed needs of the animals. This way, we aim to prevent significant losses caused by ruminal acidosis.

The fourth phase of the "CEO of the Farm" is the "Ghost Scale." Designed to reduce the workload of personnel and manage farms with fewer staff, the Ghost Scale is a sub-step of the CEO of the Farm project for busy farms.

The fifth phase of the "CEO of the Farm" is autonomous "Drone Shepherd Dogs." Stay tuned for developments...

The Influence of Artificial Intelligence Technology on the Management of Livestock Farms

Say hello to smart farms without shepherds! Farm 5.0

Livestock is of vital importance for maintaining ecological integrity and ensuring food security. The term "artificial intelligence" was first coined by John McCarthy in 1956, and now this technology is preparing to be widely used in the management of poultry, dairy, and many livestock farms. Despite decades of work and widespread application, artificial intelligence is still one of the least understood subfields. Artificial intelligence provides farmers with unparalleled support, enabling them to minimize resource use, improve the sustainability of feeding practices, and generally increase farm efficiency, especially in reducing carbon footprint. Artificial intelligence is a blessing that increases efficiency and productivity while reducing the likelihood of human error. Producers can use artificial intelligence to simulate human decision-making, interpret data collected by sensors and other hardware technologies, and provide solutions. Thanks to artificial intelligence tools, it is possible to easily track animal activities and locations, and collect data on behaviors, habitats, and health conditions. Animal identification, monitoring animal welfare, determining gender, vaccination schedules, and pasture assessment are some of the areas where artificial intelligence has been widely applied so far. Drones, robots, and blockchains are some of the automation forms commonly used in dairy farms. However, development costs requiring more infrastructure on the farm and the automation potential to replace them limit the advancement of the technology.

Especially using the latest smart drone technology, when your animals enter the feed lane or are in any part of the barn, the drone automatically takes off from the charging station at preset intervals, approaches the paddocks, and identifies each animal, capturing detailed video footage. It records individual videos of your animals' external appearances, measures their body temperatures, udder temperatures, vulva temperatures, and appearances into the database. If there is any issue, it immediately notifies you. This way, you can instantly learn how close the animal is to giving birth without being near the animal. If you wish, it attaches a short video and photos of the problematic animal and the affected area to the notification.

Artificial Intelligence in Estimating Breeding Value in Livestock
Machine learning techniques such as decision trees and artificial neural networks (ANN) are increasingly used in agriculture because they are fast, powerful, and flexible tools for classification and prediction applications, including nonlinear systems. These techniques have been used to detect mastitis, detect estrus, and explore the causes of selection. Decision trees and related methods are also used in the analysis of lactation curves, interpretation of somatic cell count data, and evaluation of reproductive management efficiency. Additionally, ANN is used to predict total agricultural milk production, 305-day milk yield, and mastitis detection. Fuzzy logic, which involves classifying variables into fuzzy sets with membership degrees between 0 and 1, has recently found a place in agricultural research. Currently, there are various contemporary groups focusing on the identification and monitoring of mastitis and estrus through automated milking systems and genetic evaluations. The main obstacle in using membership functions (MFs) is the time and computational effort required to make MF decisions. This can be reduced by using ANNs, which are relatively easy to construct. Developing a morphological evaluation using artificial intelligence involves both conceptualization and implementation of a comprehensive approach.


Developing an Artificial Intelligence Model for Livestock

Artificial intelligence has the potential to significantly increase efficiency in livestock, from identification and monitoring to predicting optimal feeding and breeding strategies.

AI-based models can be used to analyze data from various sources such as sensors, imaging, and other digital systems to provide insights into animal health and performance. AI can also be used to predict disease outbreaks, identify animal health issues, and provide early warnings of potential threats to production. Any AI model for livestock must be adaptable to the specific needs of the farm.

Therefore, the first step in developing a model is data collection, which involves gathering data that can be used to develop AI models. This may include data from sensors and cameras installed on livestock farms, as well as data from other sources such as weather and soil data. Once the data is collected, it needs to be cleaned and preprocessed before being used in the AI model development process. The second step of the AI model is data analysis. Here, the data is analyzed and visualized to gain insights into the behavior of animals and the environment. This is done by applying various techniques such as machine learning, deep learning, and natural language processing to the data to identify patterns and trends. The third step of the AI model is model development, which involves creating AI models that can be used to predict animal behavior. This may include predicting optimal feeding and breeding strategies as well as predicting future health and productivity of livestock. The fourth step of the AI model is model deployment, which involves deploying AI models to livestock farms. This can be done using various platforms such as cloud-based solutions or embedded systems. The fifth step of the AI model is model evaluation, which involves evaluating the AI models to ensure they perform as expected. This is done by collecting data from the deployed model and analyzing it to identify areas for improvement or potential risks. The sixth and final step of the AI model is model improvement, which involves using feedback from the model evaluation to improve the AI models. This may include adjusting model parameters or including new datasets to improve the model's accuracy.

In conclusion, an AI model for livestock must be adapted to the specific needs of the farm and include components such as data collection, model development, model validation, model deployment, and model maintenance. This model must be regularly monitored and updated to ensure it continues to provide accurate and reliable results.
Artificial intelligence is a crucial alternative for the management of livestock farms worldwide as it reduces the workload of managers and other farm personnel. AI is a suitable and effective technology in livestock as it optimizes resource use and efficiency. It is also a suitable technological advancement to feed the world's growing human population. It largely solves the problem of resource scarcity. Artificial intelligence and machine learning generally aim to augment human labor rather than replace it.

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