DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets

Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements

Role of SimMan in teaching clinical skills to preclinical medical students

<p>Abstract</p> <p>Background</p> <p>Simulation training has potential in developing clinical skills in pre-clinical medical students, but there is little evidence on its effectiveness.</p> <p>Methods</p> <p>Twenty four first year graduate entry preclinical medical students participated in this crossover study. They were divided into two groups, one performed chest examination on each other and the other used SimMan. The groups then crossed over. A pretest, midtest and post-test was conducted in which the students answered the same questionnaire with ten questions on knowledge, and confidence levels rated using a 5 point Likert scale. They were assessed formatively using the OSCE marking scheme. At the end of the session, 23 students completed a feedback questionnaire. Data was analyzed using one-way ANOVA and independent <it>t</it>-test.</p> <p>Results</p> <p>When the two groups were compared, there was no significant difference in the pretest and the post-test scores on knowledge questions whereas the midtest scores increased significantly (P< 0.001) with the group using SimMan initially scoring higher. A significant increase in the test scores was seen between the pre-test and the mid-test for this group (P=0.009). There was a similar albeit non significant trend between the midtest and the post-test for the group using peer examination initially.</p> <p>Mean confidence ratings increased from the pretest to midtest and then further in the post-test for both groups. Their confidence ratings increased significantly in differentiating between normal and abnormal signs [Group starting with SimMan, between pretest and midtest (P= 0.01) and group starting with peer examination, between midtest and post-test (P=0.02)]. When the students’ ability to perform examination on each other for both groups was compared, there was a significant increase in the scores of the group starting with SimMan (P=0.007).</p> <p>Conclusions</p> <p>This pilot study demonstrated a significant improvement in the students’ knowledge and competence to perform chest examination after simulation with an increase in the student’s perceived levels of confidence. Feedback from the students was extremely positive. SimMan acts as a useful adjunct to teach clinical skills to preclinical medical students by providing a simulated safe environment and thus aids in bridging the gap between the preclinical and clinical years in medical undergraduate education.</p