Biology subjects

Pastell, M.

Publications and source records attributed to Pastell, M..

5 recordsLinked to original sources

bioRxiv2025

Photosynthetic adjustments maintain lettuce growth under dynamically changing lighting in controlled indoor farming setups

Studies have uncovered delicate mechanisms that enable plant acclimation to fluctuating light. Translating the knowledge to controlled environment agriculture could advance the development of cost-effective dynamic lighting strategies, but the effects of varying light intensities on vegetable crops remain poorly understood. Here we recorded chlorophyll fluorescence, photosynthetic activity, metabolic responses, and growth of lettuce (Lactuca sativa L.) cv. Katusa under dynamic lighting. The light intensity was varied at different times of the photoperiod with uniform daily light integral. Three setups, including a plant phenotyping facility, a small-scale vertical farm testbed and a larger-scale vertical farm were utilized to address the physiological responses and scalability of lighting strategies. We found that dynamic lighting supported lettuce cv. Katusa growth in all three indoor cultivation setups, even under artificial "split-night" regimes where the photoperiod was interrupted by two periods of darkness. The lettuce plants displayed delicate adjustments in photosynthetic light reactions and carbon metabolism, the latter of which followed the cumulative daily light integral under different lighting regimes. However, the overall metabolic composition of lettuce leaves did not respond to the changing light intensities. Our findings support the conclusion that dynamic lighting enables cost-effective lighting via optimization of electricity use in indoor cultivation. HighlightPhotosynthetic adjustments maintain lettuce (Lactuca sativa L.) cv. Katusa growth under dynamic lighting. This enables cost-effective cultivation via optimization of electricity use in controlled environment agriculture.

plant biology↗

bioRxiv2022

CNN and transfer learning-based classification model for automated cows feeding behaviour recognition from accelerometer data

Due to technological developments, wearable sensors for monitoring farm animal behaviour have become cheaper, with longer life and more accessible for small farms and researchers. In this study, an acceleration measuring tag connected by BLE for monitoring behaviour of dairy cows was used. An optimal CNN-based model for the feeding behaviour classification was trained and the training process was analysed considering training dataset and the use of transfer learning. A classifier based on a neural network was trained by acceleration data collected in a research barn. Based on a dataset including 33.7 cow*days (21 cow recorded during 1-3 days) of labelled data and an additional free access dataset with similar acceleration data, a classifier with F1=93.9% was developed. The optimal classification window size was 90s. In addition, the influence of the training dataset size on the classifier accuracy was analysed for different neural networks using the transfer learning technique. During increasing of the training dataset size, the rate of the accuracy improvement decreased, and, starting from a specific point, the use of additional training data can be impractical. Relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialised model weights, and higher accuracy was achieved when transfer learning was used. These findings can be used for estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions. HighlightsCNN cow feeding behaviour classifier was optimised for neck tags. Transfer learning technique significantly improves accuracy of CNN. Relation between the training dataset size and CNN accuracy was estimated. NN trained by one dataset can be inapplicable for others. BLE tags measuring acceleration transferred data in real time.

animal behavior and cognition↗

bioRxiv2019

Mid-Infrared Spectroscopic Analysis of Raw Milk to Predict the Blood Plasma Non-Esterified Fatty Acid Concentration in Dairy Cows

In high yielding dairy cattle, severe postpartum negative energy status is often associated with metabolic and infectious disorders that negatively affect production, fertility and welfare. Mobilization of adipose tissue associated with a negative energy status is reflected through an increased level of non-esterified fatty acids (NEFA) in the blood plasma. Earlier, identification of a negative energy status through the detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently there have been attempts to predict blood NEFA concentration from milk samples. This study aimed to develop and validate a model to predict the blood plasma NEFA concentration using milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in weeks 2, 3 and 20 post-partum for 192 lactations in 3 different herds. The blood plasma samples were taken in the morning, while representative milk samples were collected during the morning and evening milk session on the same day. To predict the blood plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the two independent herds to explore their robustness and wide applicability. The final model can accurately predict blood plasma NEFA concentrations below 0.6 mmol/L with a root mean square error of prediction (RMSE) of less than 0.143 mmol/L. However, for blood plasma with more than 1.2 mmol/L NEFA, the model clearly underestimates the true level. Additionally, it was found that morning blood plasma NEFA levels were predicted with a significantly higher accuracy (p = 0.009) using MIR spectra of evening milk samples compared to morning samples, with RMSEP values of respectively 0.182 and 0.197 mmol/L and R2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for a negative energy status, indicated with detrimental morning blood plasma NEFA levels (> 0.6 mmol/L), could be identified with a sensitivity and specificity of respectively 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens opportunities for regular metabolic screening and improved resilience phenotyping.

bioengineering↗

bioRxiv2018

A Hidden Markov Model to Estimate the Feeding Time of Dairy Cows Based on Indoor Positioning Data

The feeding time of dairy cows is linked with the health status of the animal and can be used to estimate daily feed intake together with other measurements. The aim of this study was to develop a model to measure the time a dairy cow spends at a feed bunk using an Ultra wide-band indoor positioning system.\n\nWe measured the feeding behavior of 50 dairy cows during 7 days using Ubisense indoor positioning system and Insentec roughage feeders. We calculated the feeding (presence at the feeder) probability of the cow using logistic regression model with the distance to feed barrier as input and used the Viterbi algorithm to calculate the most likely state (feeding or not feeding) given state transition probabilities. The model was able to predict whether the cow was at the feeding trough or not with the accuracy of 97.6%, sensitivity 95.3% and specificity 97.9%. The model was also able to estimate the mean bout duration and the number of feeding bouts.

animal behavior and cognition↗

bioRxiv2017

Filtering Methods to Improve the Accuracy of Indoor Positioning Data for Dairy Cows

Several indoor positioning systems for livestock buildings have been developed to be used as a tool in automated animal welfare monitoring. In many environments the measurements from positioning systems still contain unwanted noise and the quality of the measurement data can be enhanced using filters.\n\nThe aim of this study was to develop an efficient filter for positioning data measured from dairy cows with UWB-based indoor positioning system in a free stall barn. We developed and tested a heuristic jump filter combined with median filter and extended Kalman filter. The performance of the filters were compared against reference data collected from Insentec Roughage intake feeders and scan sampling of animal presence in a specific lying stall with over 1500 reference observations from both methods.\n\nWe were able to improve the quality of the positioning data significantly using filtering. The 9th order median filter provided best estimates for cow position when the cows were not moving with median 100% of measurements located in correct stall and 84% in correct feeding trough when compared to the reference observations and measurements. The extended Kalman filter also improved the positioning accuracy significantly when compared to raw data and provides better of estimates of the trajectory of moving cows.

animal behavior and cognition↗