AI-based Cattle Disease Detection System Using Modified CNN Architecture for Blood Smear Image Analysis
Keywords:
Anaplasmosis, Babesiosis, Blood smear analysis, Cattle disease detection, Convolutional neural network, Deep learning, Veterinary diagnosticsAbstract
Tick-borne diseases such as babesiosis and anaplasmosis pose significant threats to cattle health, causing substantial economic losses in livestock farming. Traditional microscopic diagnosis of these diseases through blood smear analysis is time-consuming, labor-intensive, and prone to human error. This study presents an AI-based cattle disease detection system utilizing a modified convolutional neural network (CNN) architecture for automated blood smear image analysis. The proposed system implements a three-tier architecture comprising farmer, lab technician, and veterinary doctor modules, enabling seamless coordination in disease diagnosis. This modified CNN architecture incorporates optimized convolutional layers with ReLU activation, max-pooling strategies, and dropout regularization to enhance feature extraction from microscopic blood smear images. The system achieved 98.0% accuracy for babesiosis detection and 97.7% accuracy for anaplasmosis detection, with precision and recall exceeding 95%. Experimental results demonstrate that the proposed approach significantly outperforms traditional diagnostic methods and baseline CNN models, providing rapid, accurate, and cost-effective disease detection for improved cattle health management.
References
A. S. N. Trindade, et al. “TFinder App: Artificial intelligence to diagnose tick fever agents and assess parasitemia/bacteremia in bovine blood smears.” Veterinary Parasitology, vol. 334, no.110415, Feb. 2025.
V. Kittichai, et al. “A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases.” Journal of Big Data, vol. 12, no. 2, 2025.
A. Rehman, et al. “Machine learning-based detection and quantification of red blood cells in Cholistani cattle: A pilot study.” Research in Veterinary Science, vol. 189, no. 105650, Jun. 2025.
Gayathri D., et al. “Deep learning enhanced thermographic modeling for early and precise mastitis detection in Sahiwal cows.” Research in Veterinary Science, vol. 196, no. 105899, Nov 2025.
C. Senthilkumar, et al. “Early detection of lumpy skin disease in cattle using deep learning A comparative analysis of pretrained models.” Veterinary Sciences, vol. 11, no. 10, 2024.
A. Gupta, et al., “Revolutionizing cattle health: A machine learning approach to efficiently predict lumpy disease in cows.” 4th IEEE Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2023, pp. 1–6.
M. Rony, et al. “Cattle external disease classification using deep learning techniques.” 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021, pp. 1–7.
S. Mehta and S. Choudhary, “Advancements in veterinary diagnostics: A CNN-SVM model for cattle disease identification.” 4th Asian Conference on Innovation in Technology (ASIANCON), Pimari Chinchwad, India, 2024, pp. 1–5.
H. Sadeghi, et al., “Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows.” PLOS ONE, vol. 17, no. 1, Jan. 2022.
Ş. Şahinduran, et al. “A deep learning-based tool for rapid and automated detection of Cryptosporidium oocysts: A new approach for veterinary diagnostics and epizootiological surveys.” Experimental Parasitology, vol. 281, Feb. 2026.
M. Capuozzo, et al. “A dedicated deep learning workflow for automatic Fasciola hepatica and Calicophoron daubneyi egg detection using the Kubic FLOTAC microscope.” International Journal for Parasitology, vol. 56, no. 2, Feb. 2026.
B. Lake, F. Getahun, and F. T. Teshome, “Application of artificial intelligence algorithm in image processing for cattle disease diagnosis.” Journal of Intelligent Learning Systems and Applications, vol. 14, no. 4, Nov. 2022.
C. Kwenda, et al. “Mobile diagnosis of foot and mouth disease in cattle based on hybrid deep neural networks.” Conference on Information Communications Technology and Society (ICTAS), 2025.
K. A. Sharada, N. T. Najma, R. Sameer, R. Khan, and R. U. Zareen, “Detection of lumpy skin disease in cattle using IoT and deep learning techniques,” International Journal of Advanced Research in Science, Communication and Technology, vol. 3, no. 2, June 2023.
M. Pal, et al. “Implementation of a deep learning system for detection and classification of lumpy skin disease in cattle: Enhancing precision and efficiency in veterinary diagnostics.” Veterinary Medicine and Science, vol. 11, no. 6, 2025.
M. U. Farooq, M. Mubeen, and M. D. A. Qureshi, “Artificial intelligence and deep learning for automated diagnosis of bovine respiratory disease complex from infrared thermography and audio signal.” Biology and Biotechnology Communications. vol. 2 no. 1, 2024.
B. Xu, et al. “Evaluation of deep learning for automatic multi-view face detection in cattle.” Agriculture, vol. 11, no. 11, 2021.
Y. Guo, et al. “Monitoring behaviors of broiler chickens at different ages with deep learning.” Animals, vol. 12, no. 23, 2022.
L. M. Gonçalves, et al. “Computer vision analysis of luteal color Doppler ultrasonography for early and automated pregnancy diagnosis in Bos taurus beef cows.” Journal of Animal Science, vol. 103, 2025.
S. L. Gayathri, et al. “Advanced mastitis detection in Bos indicus cows: A deep learning approach integrated with infrared thermography.” Journal of Thermal Biology, vol. 131, July. 2025.
N. Shivaanivarsha, et al. “A ConvNet based real-time detection and interpretation of bovine disorders.” International Conference on Communication, Computing and Internet of Things (IC3IoT), 2022.
O. E. Olorunshola, et al. “Hybrid CNN-Haralick framework for Foot and Mouth Disease classification in cattle.” Asian Journal of Research in Computer Science, vol. 18, no. 6, 2025, pp. 390–402.
D. K. Saha, “An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows.” Heliyon, vol. 10, no. 15, 2024.
N. Gadagin, et al. “Harnessing data science for healthy herds: Deep learning for cattle disease detection.” IEEE North Karnataka Subsection Flagship International Conference (NKCon), 2024.
S. Mehta and S. Khurana. “Integrating convolutional neural networks and support vector machines for cattle disease classification.” Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 2024.
S. S. Reddy, C. Nandini, “Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images”. International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 4 pp. 649–664.
A. A. AlZubi, “Lumpy skin disease detection in cattle by a robust approach using advanced convolutional neural networks.” Indian Journal of Animal Research, vol. 58, no. 12, 2024, pp. 2146–2153.
G. Gao, et al. “CNN-Bi-LSTM: A complex environment-oriented cattle behavior classification network based on the fusion of CNN and Bi-LSTM.” Sensors, vol. 23, no. 18, 2023.
C. Nandini, et al. “Anomaly detection utilizing diffusion variational graph neural network optimized with spider wasp optimizer in medical time series data.” Biomedical Signal Processing and Control, vol. 119, pt. B, Jun. 2026, p. 109724.
S. Shinde, et al. “Cattle Disease detection using VGG16 CNN architecture.” 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1–6.
