Spotting problems early can make all the difference – whether in our daily lives or in managing the health of animals on a farm. In modern agriculture, keeping livestock healthy isn’t just about checking animals by eye anymore; new technologies are helping farmers detect health issues faster and more accurately.
One exciting development is using sound to monitor poultry. Chickens – and birds in general -communicate through vocalizations, and subtle changes in these sounds can reveal early signs of respiratory diseases. By “listening” to the flock with special microphones and combining that information with video data, farmers can catch health problems long before they become serious.
This paper presents the smart system developed by Rinisoft that uses both audio and video to monitor poultry health. Thanks to affordable cameras with built-in microphones, faster internet in rural areas, and powerful small computers, these systems can process large amounts of data in real time. As a result, farmers get a clear picture of their flock’s health and can act quickly to prevent disease, enhancing both animal welfare and farm productivity.
How AI Helps Farmers Spot Diseases Before They Spread
Imagine a chicken farm where every cluck, cough, or sneeze could signal the early stages of a disease. Detecting these signals quickly is crucial – once a respiratory illness spreads, it can devastate an entire flock. Traditionally, farmers rely on thermal cameras, CCTV, and manual observation. But crowded conditions and limited sensor resolution often mean that diseases are detected too late.
Enter the world of Digital Agriculture, where artificial intelligence (AI) is transforming the way farmers monitor animal health. Researchers at Rinisoft, through the NESTLER Project, have developed a system that listens to chickens with the precision of a medical diagnosis. By combining sound and video data, the system can detect signs of disease early – before it spreads.
The provided diagram illustrates the NESTLER Flock Health Assessment System Architecture. The process begins with data collection from a chicken coop, utilizing an HD camera and a microphone to capture real-time video and audio of the chickens.
The collected video and audio data are transmitted via Wi-Fi, Ethernet, or LTE to the Nestler Cloud. The cloud’s primary function is to process and analyze the data using AI and machine learning algorithms. This analysis aims to detect four key conditions:
- – Stress
- – Feed (issues related to feeding)
- – Parasites
- – Respiratory Diseases
The analysis determines if any of these issues are present, leading to a decision point.
If the analysis concludes that no issues or “disasters” are detected (NO), the user’s mobile app interface displays a green indicator for the specific coop area, such as “Area 5 Box #2.” This indicates that everything is normal. If the analysis identifies one or more of the monitored issues (YES), the system triggers an ALARM. The user’s app interface for that specific coop area will show a red indicator and the word “ALARM.” This alerts the user to a potential problem, allowing them to take immediate action.
How It Works
The core of the innovation lies in its advanced audio analysis. Each chicken’s vocalizations – such as sneezes, wheezes, or coughing – contain critical information about its respiratory health. The system converts these sounds into spectrograms, which are visual representations of audio, much like turning music into a colourful sheet of patterns. These images are then analysed by convolutional neural networks (CNNs), a type of AI algorithm specifically trained to recognize patterns in images.
However, audio data alone is insufficient. The NESTLER system also monitors flock movement and behaviour. Reduced activity, combined with unusual vocalizations, provides a strong signal that something is wrong. By merging audio and video data, the AI creates a quantitative health index for the entire flock. Farmers can see this information in real time through a mobile app, allowing them to intervene before the disease spreads.
This multimodal AI approach is made possible by several recent technological advancements:
-Affordable cameras with built-in microphones
-Widespread rural internet, including satellite options
-Small, powerful computing devices capable of running AI
-Advanced machine vision development frameworks.
Compared to traditional methods, this multimodal AI system is faster, more accurate, and scalable. It reduces the need for manual monitoring, lowers economic losses, and improves animal welfare.
Looking Ahead
The NESTLER Project shows that AI is a practical, valuable tool for agriculture, not just a lab concept. By “listening” to their chickens, farmers can act sooner, protect their flocks, and embrace a new era of precision livestock farming.