Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

The Internet of Things (IoT) technology has been being revolutionized in various aspects of agriculture around the world ever since. Its application has already found its success in some countries. On the contrary, this technology has yet to find its substantial breakthrough in the Philippines. This study shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom cameras and a Python-driven Web Application. The dimensions of the barn were measured, and the Cameras' Field of Views (FOVs) were pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The results will be sent to the cloud server to display on the web application for analysis. The web app can allow updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. Based on the results, the program performed satisfactorily at 50% detection efficiency

Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

The Internet of Things (IoT) technology has been being revolutionized in various aspects of agriculture around the world ever since. Its application has already found its success in some countries. On the contrary, this technology has yet to find its substantial breakthrough in the Philippines. This study shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom cameras and a Python-driven Web Application. The dimensions of the barn were measured, and the Cameras' Field of Views (FOVs) were pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The results will be sent to the cloud server to display on the web application for analysis. The web app can allow updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. Based on the results, the program performed satisfactorily at 50% detection efficiency

NEWAppendix 4_morpho_FINAL

Phylogenetic tree based on 156 morphological character

NewAppendix_8_H3_FINAL2

Maximum Likelihood tree based on H3 dat

Appendix 3_morphomatrix

Morphomatrix used in combined Bayesian analysi

Data from: The emergence of the lobsters: phylogenetic relationships, morphological evolution and divergence time comparisons of an ancient group (Decapoda: Achelata, Astacidea, Glypheidea, Polychelida)

Lobsters are a ubiquitous and economically important group of decapod crustaceans that includes the infraorders Polychelida, Glypheidea, Astacidea and Achelata. They include familiar forms such as the spiny, slipper, clawed lobsters and crayfish and unfamiliar forms such as the deep-sea and “living fossil” species. The high degree of morphological diversity among these infraorders has led to a dynamic classification and conflicting hypotheses of evolutionary relationships. In this study, we estimated phylogenetic relationships amongst the major groups of all lobster families and 94% of the genera using 6 genes (mitochondrial and nuclear) and 195 morphological characters across 173 species of lobsters for the most comprehensive sampling to date. Lobsters were recovered as a non-monophyletic assemblage in the combined (molecular + morphology) analysis. All families were monophyletic, with the exception of Cambaridae, and 7 of 79 genera were recovered as poly- or paraphyletic. A rich fossil history coupled with dense taxon coverage allowed us to estimate and compare divergence times and origins of major lineages using two drastically different approaches. Age priors were constructed and/or included based on fossil age information or fossil discovery, age, and extant species count data. Results from the two approaches were largely congruent across deep to shallow taxonomic divergences across major lineages. The origin of the first lobster-like decapod (Polychelida) was estimated in the Devonian (~409-372 million years ago (Ma)) with all infraorders present in the Carboniferous (~353-318 Ma). Fossil calibration subsampling studies examined the influence of sampling density (number of fossils) and placement (deep, middle, shallow) on divergence time estimates. Results from our study suggest including at least 1 fossil per 10 operational taxonomic units (OTUs) in divergence dating analyses

NEWAppendix 1_Jan_2014

Taxonomy, voucher catalog numbers, and GenBank accession numbers for gene sequences used in study. An “N/A” (not available) indicates missing sequence data

NEWAppendix_9_IGTP_FINAL2

IGTP Species Tree Analysis for all taxa in this stud

NewAppendix 12_fossil justification

Justification for each fossil included in the 28 fossil divergence time analysis

Appendix 2_ATOL Lobsters morpho list

Morphological characters and states used in combined Bayesian analysis