Early Detection of Avian Diseases Based on Thermography and Artificial Intelligence

SIMPLE SUMMARY: From an economic point of view, timely information about the flock state is crucial for poultry farmers. When a flock is infected with a disease, if quick and necessary measures are not taken, the disease will spread and affect the whole flock. Artificial intelligence is one of the popular methods in precision livestock farming and is effective in various fields such as weight measurement, feed intake estimation, and disease diagnosis. So far, chicken disease has been diagnosed using sound signal processing and video recordings. This study attempted to develop a new and rapid method of poultry disease diagnosis based on thermography for data collection and artificial intelligence for data analytics. With the proposed method, Avian Influenza and Newcastle Disease can be detected within 24 h after virus infection. ABSTRACT: Non-invasive measures have a critical role in precision livestock and poultry farming as they can reduce animal stress and provide continuous monitoring. Animal activity can reflect physical and mental states as well as health conditions. If any problems are detected, an early warning will be provided for necessary actions. The objective of this study was to identify avian diseases by using thermal-image processing and machine learning. Four groups of 14-day-old Ross 308 Broilers (20 birds per group) were used. Two groups were infected with one of the following diseases: Newcastle Disease (ND) and Avian Influenza (AI), and the other two were considered control groups. Thermal images were captured every 8 h and processed with MATLAB. After de-noising and removing the background, 23 statistical features were extracted, and the best features were selected using the improved distance evaluation method. Support vector machine (SVM) and artificial neural networks (ANN) were developed as classifiers. Results indicated that the former classifier outperformed the latter for disease classification. The Dempster–Shafer evidence theory was used as the data fusion stage if neither ANN nor SVM detected the diseases with acceptable accuracy. The final SVM-based framework achieved 97.2% and 100% accuracy for classifying AI and ND, respectively, within 24 h after virus infection. The proposed method is an innovative procedure for the timely identification of avian diseases to support early intervention.