A New Way to Identify Mastitis in Cows Using Artificial Intelligence

• Published in: AgriEngineering 2024-12, Vol.6 (4), p.4220-4232
• Main Authors: Silva, Rodes Angelo Batista da, Pandorfi, Héliton, Cordeiro, Filipe Rolim, Soares, Rodrigo Gabriel Ferreira, Medeiros, Victor Wanderley Costa de, Almeida, Gledson Luiz Pontes de, Barbosa Filho, José Antonio Delfino, Marinho, Gabriel Thales Barboza, Silva, Marcos Vinícius da
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Animals; Artificial intelligence; Automation; Cattle; Classification; Computer vision; convolutional neural network; Dairy cattle; Dairy farming; Dairy farms; Dairy industry; Datasets; Decision making; Deep learning; image analysis; infrared thermography; Knowledge management; Machine learning; Mastitis; Medical imaging; Neural networks; Optimization; Software; Temperature; Thermography; Transfer learning
• ISSN: 2624-7402; 2624-7402
• DOI: 10.3390/agriengineering6040237

Mastitis is a disease that is considered an obstacle in dairy farming. Some methods of diagnosing mastitis have been used effectively over the years, but with an associated relative cost that reduces the producer’s profit. In this context, this sector needs tools that offer an early, safe, and non-invasive diagnosis and that direct the producer to apply resources to confirm the clinical picture, minimizing the cost of monitoring the herd. The objective of this study was to develop a predictive methodology based on sequential knowledge transfer for the automatic detection of bovine subclinical mastitis using computer vision. The image bank used in this research consisted of 165 images, each with a resolution of 360 × 360 pixels, sourced from a database of 55 animals diagnosed with subclinical mastitis, all of which were not exhibiting clinical symptoms at the time of imaging. The images utilized in the sequential learning transfer were those of MammoTherm, which is used for the detection of breast cancer in women. The optimized model demonstrated the most optimal network performance, achieving 92.1% accuracy, in comparison to the model with manual search (86.1%). The proposed predictive methodologies, based on knowledge transfer, were effective in accurately classifying the images. This significantly enhanced the automatic detection of both healthy animals and those diagnosed with subclinical mastitis using thermal images of the udders of dairy cows.