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Big Data Analytics Powering Progress in Animal Agriculture

Harsh Arora, Content Consultant and Animal-Assisted Social Worker
| Posted at 3:06 PM by ISACA News | Category: Cloud Computing | Permalink | Email this Post | Comments (0)

There has been significant progress in technologies that can be utilized in the livestock industry. These technologies will help farmers, breeders associations and other industry stakeholders in continuously monitoring and collecting animal-level and farm-level data using less labor-intensive approaches.

Specifically, we are seeing the use of fully automated data recording based on digital images, sounds, sensors, unmanned systems and real-time uninterrupted computer vision. These technologies can help farmers tremendously and have the potential to enhance product quality, well-being, management practice, sustainable development and animal health, and ultimately contribute to better human health.

These technologies, when implemented with rich molecular information such as transcriptomics, genomics, and microbiota from animals, can help achieve the long-lasting dream of implementing precision animal agriculture. What this means is, with the help technology, we will be able to better monitor and manage an individual animal with tailored information.

However, the complexity of data generated and its growing volume, by the fully automated data recording or phenotyping platforms mentioned above, leads to several hindrances in the successful implementation of precision animal agriculture.

How Machine Learning and Data Mining Helps
The growing areas of machine learning and data mining are expected to help meet the challenges faced in global agriculture.

When combined with big data, machine learning models can be used as a framework for biology. However, as mentioned above, models of highly complex data usually suffer from overfitting, when we train it with a lot of data. Overfitting is the biggest problem in the failure of naive applications with complex models.

The primary reasons for applying machine learning techniques to animal science are:

  1. To build prior knowledge for regularization with continued efforts
  2. To continuously gather data sets and integrate data sets with different modalities to increase the size of the collected samples that can be utilized for training

After collecting the data, one has to keep in mind the computational load that is required to analyze the chunks of integrated data sets. Whenever possible, one should also consider the compatibility of the model with parallel computing.

For example, GPU cloud computing services offered by Amazon AWS and Microsoft Azure might prove useful. They also provide infrastructures to secure, host and share big data. With the guidance of machine learning and data methods, one can reach the next phase of growth in big data to reconsider all characteristics of management decisions in the animal sciences.

In conclusion, precision animal agriculture is bound to rise in the livestock enterprise in the domains of production, management, welfare, health surveillance, sustainability and environmental footprint. Significant progress has been made in the utilization of tools to regularly monitor and collect information from farms and animals in a less tedious manner than before.

With these methods, the animal sciences have embarked on a journey to improve animal agriculture with information technology-driven discoveries. The problem of overfitting can be dealt with by utilizing popular cloud platforms like AWS and Azure.

About the author: Harsh Arora is a proud father of four rescued dogs and a leopard gecko. Besides being a full-time dog father, he is a freelance content writer/blogger and a massage expert who is skilled in using the best massage gun to deliver the best results.

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