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ADVANCING POULTRY HEALTH
Artificial intelligence (AI) is reshaping the future of various industries, and the poultry sector is no exception.
Machine learning (ML) and predictive analytics are both segments of Artificial intelligence (AI). AI predictive analytics use machine learning (ML) techniques and models that acquire knowledge from data over time.
These models undergo training using past data in order to recognize and analyze patterns and interactions (Ravi et al., 2018).
After being trained, the models are utilized to create predictions about future outcomes using new, previously unseen data (Figure 1).
Figure 1. Simple depiction of predictive analytics (Source: Ogirala et al., 2024).
Disease and inadequate hygiene are two of the many problems that confront the chicken production sector. Coccidiosis, Newcastle, Gumboro pullorum, and Salmonella are among the most prevalent diseases (Machuve et al., 2022).
The diagnostic testing for these diseases can be expensive, time-consuming, and laborious.
Bacteriological testing on chicken excrement, for instance, can cost an average of $30 from American laboratories (e.g., GPLN and others), with pricing fluctuating based on the quantity of birds tested (GPLN, 2024; CEVDL, 2024).
Keeping a close eye on certain birds for any changes in behavior or appearance can help poultry workers quickly pinpoint and eliminate the disease’s cause.
This is where predictive analytics have provided a breakthrough solution for the poultry industry.
Farm managers can take appropriate measures even before the diseases manifest clinically, thanks to predictive analytics models that derive results from historical data and real-time inputs to forecast outbreaks.
Modern analytics, in the form of machine learning algorithms and powered by big data technologies, combines these two imperatives by enabling an analysis that can both detect patterns today and predict future diseases.
DATA COLLECTION AND MANAGEMENT
The key to successful predictive analytics in poultry farming is the collection and management of different data.
The prominent data types on the farm include environmental factors like temperature, humidity, and air quality (key for poultry health).
Tracking them helps to forecast conditions that may favor a disease outbreak (Jung et al., 2021; Johansen et al., 2019).
Moreover, sensors and video surveil...
