Estimation of Japanese Black Calf Manure Moisture and Possibility of Classifications of Manure Score using Deep Learning

Abstract

Cattle manure is scored to indicate cattle health. Scoring is done by visual observation related to images and appearance features. Nevertheless, the score standard remains obscure. Few reports have described numerical indexes for this score. This study examined quantification of manure characteristics, which is the basis of cattle health judgment to reduce burdens on livestock managers and to facilitate skill acquisition. We investigated cattle manure moisture characteristics by measuring the moisture contents of manure sampled from Japanese black calves. Subsequently, we verified the classification accuracy of manure images based on manure moisture characteristic using deep learning object detection. The range of manure moisture contents was 75.7–93.8%. Manure with moisture contents of 89–91% is spread widely on the bedding. Manure with moisture contents of 92–94% is in a liquid state. Therefore, this manure went under the bedding （rice husk）. The bedding covered the manure. Scores were divided into three levels with moisture content of 6%, four levels with 5%, five levels with 4%, and six levels with 3%.　Then, the AI models were made. The F1 score of the AI model was 0.80 for three classification levels, 0.73 for four levels, 0.62 for five levels, and 0.53 for six levels. The F1 score of the AI model for three classification level and four level were significantly higher than the AI model for five level and fix level. So, when the manure score classifies by deep learning, three classification level, and four level are effective.