Abstract:
Due to their value as a food source, fiber, and other products globally, there has
been a growing focus on the wellbeing and health of small ruminants, particularly
in relation to anemia induced by blood-feeding gastrointestinal parasites like
Haemonchus contortus. The objective of this study was to assess the packed cell
volume (PCV) levels in blood samples from small ruminants, specifically goats,
and create an efficient biosensor for more convenient, yet accurate detection
of anemia for on-farm use in agricultural environments for animal production
optimization. The study encompassed 75 adult male Spanish goats, which underwent
PCV testing to ascertain their PCV ranges and their association with anemic
conditions. Using artificial intelligence-powered machine learning algorithms,
an advanced, easy-to-use sensor was developed for rapidly alerting farmers as
to low red blood cell count of their animals in this way to enable timely medical
intervention. The developed sensor utilizes a semi-invasive technique that requires
only a small blood sample. More precisely, a volume of 30 μL of blood was placed
onto Whatman filter paper No. 1, previously soaked with anhydrous glycerol.
The blood dispersion pattern on the glycerol-infused paper was then recorded
using a smartphone after 180 s. Subsequently, these images were examined in
correlation with established PCV values obtained from conventional PCV analysis.
Four separate machine learning models (ML) supported models, namely support
vector machine (SVM), K-nearest neighbors (KNN), backpropagation neural network
(BPNN), and image classification-based Keras model, were created and assessed
using the image dataset. The dataset consisted of 1,054 images that were divided
into training, testing, and validation sets in a 70:20:10 ratio. The initial findings
indicated a detection accuracy of 76.06% after only 10 epochs for recognizing
different levels of PCV in relation to anemia, ranging from healthy to severely
anemic. This testing accuracy increased markedly, to 95.8% after 100 epochs
and other model parameter optimization. Results for SVM had an overall F1 score
of 74–100% in identifying the PCV range for blood pattern images representing
healthy to severely anemic animals, and BPNN showed 91–100% accuracy in
identifying the PCV range for anemia detection. This work demonstrates that
AI-driven biosensors can be used for on-site rapid anemia detection. Optimized
machine learning models maximize detection accuracy, proving the sensor’s validity and rapidity in assessing anemia levels. This breakthrough will allow farmers, with rapid results, to increase animal wellbeing and agricultural productivity.
