A computer vision approach to improving cattle digestive health by the monitoring of faecal samples.

The digestive health of cows is one of the primary factors that determine their well-being and productivity. Under- and over-feeding are both commonplace in the beef and dairy industry; leading to welfare issues, negative environmental impacts, and economic losses. Unfortunately, digestive health is difficult for farmers to routinely monitor in large farms due to many factors including the need to transport faecal samples to a laboratory for compositional analysis. This paper describes a novel means for monitoring digestive health via a low-cost and easy to use imaging device based on computer vision. The method involves the rapid capture of multiple visible and near-infrared images of faecal samples. A novel three-dimensional analysis algorithm is then applied to objectively score the condition of the sample based on its geometrical features. While there is no universal ground truth for comparison of results, the order of scores matched a qualitative human prediction very closely. The algorithm is also able to detect the presence of undigested fibres and corn kernels using a deep learning approach. Detection rates for corn and fibre in image regions were of the order 90%. These results indicate the potential to develop this system for on-farm, real time monitoring of the digestive health of individual animals, allowing early intervention to effectively adjust feeding strategy.

Soluble phenolic compounds in fresh and ensiled orchard grass (Dactylis glomerata L.), a common species in permanent pastures with potential as a biomass feedstock.

High-value coproducts can greatly improve the feasibility of utilizing plant feedstocks for biorefining and biofuel production. Plant polyphenolics have potential application in the pharmaceutical and cosmetic industries. Orchard grass varieties have been noted for accumulation of polyphenolic compounds, and the current study determined the soluble phenol profile and content in the orchard grass variety 'Abertop'. Hydroxycinnamates and flavonoids were monitored during the transition from vegetative to flowering stage at maximum crop yield. Caffeic acid derivatives, related to bioactives in the Asian medicinal herb Salvia miltiorrhiza , and novel hydroxycinnamate-flavone conjugates were also identified in extracts. Harvest yields of hydroxycinnamates and flavonoids ranged from 2.6 to 4.0 kg/ha and from 2.1 to 5.1 kg/ha, respectively. Abundant compounds showed high levels of antioxidant activity comparable with that of trolox. Minimal changes in soluble phenol content and composition were observed after ensiling with the exception of increases in caffeic acid, a caffeic acid derivative, and a caffeic acid breakdown product, dihydroxystyrene.

Modelling real-time simultaneous saccharification and fermentation of lignocellulosic biomass and organic acid accumulation using dielectric spectroscopy.

Dielectric spectroscopy (DS) is routinely used in yeast and mammalian fermentations to quantitatively monitor viable biomass through the inherent capacitance of live cells; however, the use of DS to monitor the enzymatic break down of lignocellulosic biomass has not been reported. The aim of the current study was to examine the application of DS in monitoring the enzymatic saccharification of high sugar perennial ryegrass (HS-PRG) fibre and to relate the data to changes in chemical composition. DS was capable of both monitoring the on-line decrease in PRG fibre capacitance (C=580 kHz) during enzymatic hydrolysis, together with the subsequent increase in conductivity (G=580 kHz) resulting from the production of organic acids during microbial growth. Analysis of the fibre fractions revealed >50% of HS-PRG lignocellulose had undergone enzymatic hydrolysis. These data demonstrated the utility of DS biomass probes for on-line monitoring of simultaneous saccharification and fermentation (SSF).