review of applications of artificial intelligence in diagnosing coccidiosis in poultry

The poultry industry is currently facing a surge in demand for its products, necessitating elevated production levels. disease control is paramount, particularly in the case of coccidiosis, a burdensome ailment. artificial intelligence (ai) has emerged as a valuable tool for controlling, preventing, and diagnosing this disease. ai encompasses technologies that empower computers to undertake tasks beyond human capacity. machine learning, a subset of ai, deploys algorithms to assimilate information, thus facilitating improved decision-making. traditionally, the diagnosis of coccidiosis relied on manual counting and pcr methods. the integration of ai has not only expedited these processes but also enhanced accuracy and accessibility. advanced techniques such as deep learning, including models like yolov3 and resnet50, the efficientnetb7 model, and cnn neural networks have proven effective for distinguishing between healthy and unhealthy samples in fecal images. early detection methods that analyze volatile organic compounds (vocs) and biomarkers, coupled with miniaturized mass spectrometry and computer vision, aid in identifying afflicted birds. furthermore, the development of a user-friendly mobile application named kuku, primarily for image analysis, demonstrates the expanding role of ai in this domain. the utilization of ai for fecal image analysis notably enhances disease diagnosis accuracy in the poultry sector, thereby significantly benefiting the economy. this technology reduces dependence on highly skilled veterinarians and minimizes diagnostic errors. moreover, leveraging ai for the early detection of diseases can reduce unnecessary antibiotic use, mitigating drug resistance and lowering associated costs. consequently, the integration of ai offers substantial advantages for the poultry industry and the broader economy.