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Livestock monitoring

Introduction

The agriculture industry is mainly based on livestock farming which means that the health and behaviour of animals have to be monitored on a regular basis. This paper aims at finding out how the process of livestock monitoring can be made easier and less costly through the use of Artificial Intelligence (AI) specifically through Computer Vision (CV). Livestock monitoring is a strenuous activity that demands a lot of manpower to ensure that the animals are in good condition, bred well, and are productive. CV AI has changed this by introducing them to advanced, accurate, and swift methods of livestock management. CV AI is an application of algorithms that analyse images and videos to understand the visual information that is captured by cameras which allows for the management of the behaviour, health and productivity of livestock.

Challenges

Livestock monitoring is not an easy task as it seems. Proactive monitoring is a strenuous activity that needs constant supervision by humans. It is also tough to notice the changes in the animal behaviour or the early signs of illness. There is no proper way of determining the actual body condition of an animal thus leading to either over feeding or under feeding. Other challenges include; inefficient breeding management, challenge in identifying specific animals in large herds and inability to monitor the livestock well in harsh weather conditions or at night. However, traditional methods are usually stressful for the animals and hence have adverse effect on their well-being and output.

AI Solutions

This is where Computer Vision AI comes in as a problem solver. This is because CV AI uses sophisticated algorithms to process visual information thus enable tracking of animal behaviour with ease and hence any changes that may be indicative of disease or stress. The use of computer vision AI in the development of body condition scoring systems has made it possible to make better and more accurate assessments of the animal’s body condition thus enhancing feeding efficiency. With CV AI it is possible to track specific animals in large herds, which helps in planning for breeding and disease control. Also, the CV AI systems can work under different light conditions and weather condition which makes the monitoring to be efficient. Cargill and Cainthus among other organizations have implemented the use of AI-mediated facial recognition system to track and monitor individual livestock.

Benefits

There is a large number of advantages of CV AI in livestock management. It cuts down on the labour associated with conventional monitoring thus being more efficient. Detection of diseases and stress in the early stages helps in the treatment of the animal and thus saves costs. Better body condition scoring helps in improving feed conversion ratio and thus reduces the cost of feed and the impact on the environment. Enhanced breeding control leads to higher production and profit margins. Real time monitoring helps in containing diseases effectively and thus prevents spread of diseases. Also, the automated system helps in minimizing any form of stress that the animals may be subject to.

Return on Investment

The ROI of CV AI in livestock monitoring can be very valuable as the implementation of the system can reduce labor costs, detect diseases earlier, increase feed conversion rate, and enhance the management of the breeding process. Higher productivity and improved animal care can only increase the profits. The ROI will vary with the particular application and situation, yet, examples from firms such as HerdX and Connecterra suggest that there is a high potential for ROI. For instance, Connecterra’s Intelligent Dairy Farmer's Assistant (IDA) is said to have boosted the efficiency of dairy farmers by 30%, while HerdX’s system has cut down on feed expenses by 20%.

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