IoT Based Automated Poultry Farm for Layer Chicken using Artificial Intelligence Techniques
Sharan Kumar, Piyush Kumar Pareek, P Rashmi, R Deepak, Vishwanath Petli
Abstract
The poultry business has a considerable impact on the food manufacturing sector. Poultry hens are in high demand, but there are growing worries over their quality in many parts of the world. Quality control in the poultry sector helps to ensure a steady supply of eggs and meat. The industry’s stakeholders benefit from recent technical developments in the tracking and monitoring of bird health. To address this issue, the authors present a system that uses a combination of a (LSTM-RF) to extract features from chicken images in an effort to boost identification accuracy. In order to test the efficacy of the suggested LSTM-RF despite the imbalanced dataset, many data improvement strategies were developed. When applied to chicken age detection, the suggested model increases accuracy to 95%, as shown by the final testing results. In addition to the aforementioned mobile-ready detection software, this study also presents the design of a comprehensive image-acquisition system for aviaries. Also, a more accurate and high-performing classification approach is provided by comparing the accuracy of several classification models. The main goalmouth of the investigate is to deliver a prognostic service framework based on the Industrial Internet of Things (IoT) that can more precisely categorise fowl hens in real time.