From the outset, the singular focus of the research was to develop an algorithm or a model or a program that would help in early and accurate detection of Mastitis – a disease in dairy cattle that needs to be combated effectively due to its biological as well as economic impact. Various studies have been undertaken over the years that focus on the problem. Before this study, the best we had was three classified clusters to chart the progression of the disease viz. Healthy, Moderately Ill and Severely Ill.
The aim at the beginning of this research was to perform a comprehensive study in terms of progression of the disease. The intent was to try and identify enhanced clusters structure where the boundaries were more lucid than the current scenario. The integral part of this research was to capture chances of recovery of cattle and estimate the accurate time where the preventive measures need to be taken within the enhanced cluster structure.
The above was achieved by following a focused yet selective cognitive approach. Thus, an analysis was performed where a “critical area” or the “transition phase” was identified. This phase indicated an area where the healthy cattle advance towards a sickness stage. This is an intermediary stage between the clusters obtained in the previous studies.
The analysis was performed using neural networks. A neural network has the capability to mimic the functioning of a brain, which makes it very useful for determining unknown patterns in real-time problems. Also, biologically, the spectrum of a disease can never be fixed in nature. Naturally, there will be a number of stages through which the disease would be progressing through. This makes it virtually impossible for a living being (in this case – cattle) to be in a perfectly healthy or perfectly sick stage at all times. Therefore, fuzziness is associated with this particular problem at all times. This analysis made us adopt neuro-fuzzy approaches for this research.
The selection of appropriate number of stages to identify overall spectrum of the disease was a very crucial step in this application. Ward clustering proved to be really comprehensive for cutting through this step. After combining all these methods together, a spectrum was obtained which depicts the different stages that a cow encounters in its progression from being healthy to sick. It was validated by tracing the sick cows in the obtained map as well.
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This spectrum depicts 13 stages through which a cow traverses during the period when it is advancing towards the sickness. This actually reflected the sick cow in different stages. Using this model, a cow could be tracked at an early stage of the disease, and treatment could come into effect rather soon. The model thus produced can be used with confidence to produce alerts for the detection of mastitis at very early stages.
The practical implementation of the research will help in saving the number of dairy cattle by timely recognition of the disease and administering the treatment to infected cattle at an early stage. Thus, apart from the enhanced longevity of the cattle, it will also help in maintaining a diverse gene-pool. The modelling approach is transferrable to other similar diseases for monitoring the progression and will help in ensuring the ability of cattle to have better immunity. The quality of cattle will increase and so will be the milk production. This will then have further impact on dairy products manufactured from the milk.
This is not only valuable from biological perspective but also from monetary perspective. The financial losses incurred every year, due to Mastitis disease, are of mammoth proportions. In New Zealand alone, the losses aggregate up to 180 million dollars. The outcome of this result – the identification of “Critical Areas” and its implementation to identify the progression of cattle health – has an immense potential to contribute to the reduction of this loss significantly.
Thus, I believe, the research outcome has the capability to be practically viable in terms of implementation as well as the benefits gleaned from the same. The model produced can be used with confidence to produce alerts for the detection of mastitis at very early stages. However, adding a few more informative measures (e.g. age of the cow etc.) might help in improving the model.
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