Haemonchus contortus infection is a major cause of anemia in goats and can reduce productivity, reproductive performance, and animal welfare. The FAMACHA© system supports on-farm anemia screening by visually assessing ocular conjunctival color, but scoring can vary with evaluator experience, and lighting conditions. This study developed a smartphone-oriented deep learning framework for FAMACHA©-based anemia screening in goats using ocular conjunctival images. The dataset included 1995 FAMACHA©-labeled images from 141 meat and dairy goats collected across three acquisition campaigns. For the binary screening task, FAMACHA© scores of 4–5 were assigned to the anemic screening class, whereas scores of 1–3 were assigned to the non-anemic screening class. The pipeline combined U-Net-based conjunctiva segmentation with classification using six ImageNet-pretrained backbones: ResNet-50, ViT-B/16, Swin-T, EfficientNet-B0, MobileNetV4-Small, and MobileViT-v2-1.0. Models were evaluated using animal-level dataset splitting, nested cross-validation, validation-selected thresholds, goat-level cluster bootstrap confidence intervals and goat-block paired permutation tests. U-Net with a ResNet-34 encoder achieved a foreground IoU of 0.7342 and a Dice coefficient of 0.8372 for conjunctiva segmentation. On the held-out test set, ViT-B/16 achieved the highest anemia recall (0.924), accuracy (0.861), and anemia-class F1-score (0.879). However, MobileNetV4-Small performed closely, with 0.847 accuracy, a 0.861 F1-score, and the highest specificity (0.825). U-Net-ResNet34 combined with MobileNetV4-Small yielded the lowest estimated combined inference latency of 479.04 ms per image, calculated from separately measured segmentation and classification latencies on a Samsung SM-A556E smartphone using TensorFlow Lite. A Pearson’s correlation analysis showed a strong positive association between fecal egg count and FAMACHA© score (r = 0.7082, R² = 0.5015, p < 0.0001). These results suggest that ocular-conjunctival imaging can support FAMACHA©-based anemia screening in goats, with ViT-B/16 serving as the strongest recall-oriented model and MobileNetV4-Small as a practical candidate for low-latency on-device inference under the tested smartphone configuration.

Background/Objectives: Animal health remains a critical issue that directly impacts economic sustainability through animal welfare and production. In small ruminants, the gastrointestinal parasite Haemonchus contortus can lead to anemia and possibly mortality, since parasite burden can be considerable and is challenging to control. Small ruminant health can be affected by poor diet and environmental conditions that lead to changes in the metabolic balance. The link between animal health and metabolic profiles has been investigated in the past. These studies have shed important light on physiological changes by identifying dietary and disease biomarkers. This study aimed to correlate the metabolite signature of feces from goats, having two levels of Haemonchus contortus infection, grazing on two different forages (Bermudagrass and Sunn Hemp). Methods: Fecal samples were taken from goats grazing either Sunn Hemp or Bermudagrass pastures, with naturally variable Haemonchus contortus loads. Samples were evaluated using ¹H-NMR and LC/MS methods to describe and compare metabolic patterns under varied forage conditions between low and high Fecal Egg Count (FEC). Results: Our findings indicated no significant difference using univariate analyses but identified 10 discriminatory features using multivariate analyses for Bermudagrass and Sunn Hemp using ¹H-NMR. With LC-MS, we found 14 significantly different features (p < 0.05, FC > 2), 115 discriminatory features for Bermudagrass and 113 in Sunn Hemp from multivariate analyses. Combining the findings of the two approaches suggested that Haemonchus contortus influenced several pathways associated with the metabolism of amino acids and energy conversion. Conclusions: The analysis of metabolome changes across both forages may help in revealing novel knowledge and accurate identification of possible biomarkers for gastrointestinal parasites. Further study is needed to validate the potential biomarker before deploying diagnostic tools based on the metabolomics indicators for early parasite diagnosis.

The journal retracts the article titled “Malted barley as a potential feed supplementation for the reduction of enteric methane emissions, rumen digestibility, and microbiome community changes in laboratory conditions”