Enhancement of observability using Kubernetes operator

Observability is the ability for us to monitor the state of the system, which involves monitoring standard metrics like central processing unit (CPU) utilization, memory usage, and network bandwidth. The more we can understand the state of the system, the better we can improve the performance by recognizing unwanted behavior, improving the stability and reliability of the system. To achieve this, it is essential to build an automated monitoring system that is easy to use and efficient in its working. To do so, we have built a Kubernetes operator that automates the deployment and monitoring of applications and notifies unwanted behavior in real time. It also enables the visualization of the metrics generated by the application and allows standardizing these visualization dashboards for each type of application. Thus, it improves the system's productivity and vastly saves time and resources in deploying monitored applications, upgrading Kubernetes resources for each application deployed, and migration of applications.

DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming

The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

Develop an automated monitoring system that allows the creation of an efficient hydroponic ecosystem, which increases the production of lettuce per square meter

The agricultural production methods of the last decades, have stood out for the use of the spaces, leaving aside even the land, mediating the greenhouses; in order to protect crops from climate variations, pests, raising their quality through better physicochemical characteristics and longer shelf life. The purpose of this work is to develop an automated system by means of materials such as sensors and microcontrollers capable of controlling physicochemical variables in a greenhouse, in order to provide the concentrations of nutrients, for the creation of an efficient hydroponic ecosystem, and standardized for an increase to production, in the cultivation of Romain variety lettuce. It is important to point out that within the hydroponic system, the Romain lettuce variety is harvested, obtaining larger products with an approximate weight of 1200 to 1500 g per piece, compared to those grown by the traditional method whose weights range between 1100 to 1300 g per piece, with a shelf life of 8 days in refrigeration.

Developing an Automated Monitoring System for Cable Yarding Systems

Cable yarders are often the preferred harvesting system when extracting trees on steep terrain. While the practice of cable logging is well established, productivity is dependent on many stand and terrain variables. Being able to continuously monitor a cable yarder operation would provide the opportunity not only to manage and improve the system, but also to study the effect on operations in different conditions.This paper presents the results of an automated monitoring system that was developed and tested on a series of cable yarder operations. The system is based on the installation of a Geographical Navigation Satellite System (GNSS) onto the carriage, coupled with a data-logging unit and a data analysis program. The analysis program includes a set of algorithms able to transform the raw carriage movement data into detailed timing elements. Outputs include basic aspects such average extraction distance, average inhaul and outhaul carriage speed, but is also able to distinguish number of cycles, cycle time, as well as break the cycles into its distinct elements of outhaul, hook, inhaul and unhook.The system was tested in eight locations; four in thinning operations in Italy and four clear-cut operations in New Zealand, using three different rigging configuration of motorized slack-pulling, motorized grapple and North Bend. At all locations, a manual time and motion study was completed for comparison to the data produced by the newly developed automated system. Results showed that the system was able to identify 98% of the 369 cycles measured. The 8 cycles not detected were directly attributed to the loss of GNSS signal at two Italian sites with tree cover. For the remaining 361 cycles, the difference in gross cycle time was less than 1% and the overall accuracy for the separate elements of the cycle was less than 3% when considered at the rigging system level. The study showed that the data analyses system developed can readily convert GNSS data of the carriage movement into information useful for monitoring and studying cable yarding operations.