Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction.

Confronting Deep-Learning and Biodiversity Challenges for Automatic Video-Monitoring of Marine Ecosystems

With the availability of low-cost and efficient digital cameras, ecologists can now survey the world’s biodiversity through image sensors, especially in the previously rather inaccessible marine realm. However, the data rapidly accumulates, and ecologists face a data processing bottleneck. While computer vision has long been used as a tool to speed up image processing, it is only since the breakthrough of deep learning (DL) algorithms that the revolution in the automatic assessment of biodiversity by video recording can be considered. However, current applications of DL models to biodiversity monitoring do not consider some universal rules of biodiversity, especially rules on the distribution of species abundance, species rarity and ecosystem openness. Yet, these rules imply three issues for deep learning applications: the imbalance of long-tail datasets biases the training of DL models; scarce data greatly lessens the performances of DL models for classes with few data. Finally, the open-world issue implies that objects that are absent from the training dataset are incorrectly classified in the application dataset. Promising solutions to these issues are discussed, including data augmentation, data generation, cross-entropy modification, few-shot learning and open set recognition. At a time when biodiversity faces the immense challenges of climate change and the Anthropocene defaunation, stronger collaboration between computer scientists and ecologists is urgently needed to unlock the automatic monitoring of biodiversity.

Automatic Monitoring System Designed for Controlling the Stability of Underground Excavation

Ensuring the stability of mining excavations is a crucial aspect of underground mining. For thispurpose, appropriate shapes, dimensions, and support of workings are designed for the given mining andgeological conditions. However, for the proper assessment of the adequacy of the used technical solutions,and the calibration of the models used in the support design, it is necessary to monitor the behavior of theexcavation. It should apply to the rock mass and the support. The paper presents the automatic systemdesigned for underground workings monitoring, and the example of its use in the heading. Electronicdevices that measure the rock mass movements in the roof, the load on the standing support, and on bolts,the stress in the rock mass, are connected to the datalogger and can collect data for a long of time withoutany maintenance, also in hard-to-reach places. This feature enables the system to be widely used, inparticular, in excavations in the vicinity of exploitation, goafs, or in the area of a liquidated exploitationfield.

Environmental Critical Aspects of The Conversion of Biomass to Biogas for Sustainable Energy in Indonesia

Renewable energy will become the foundation for meeting the world's energy needs in the future. However, Indonesia has not done much research on the development and application of technology for sustainable energy. Indonesia has potential energy sources. However, biomass conversion into other forms of energy, such as biogas, will hurt the environment. The development of biomass-based bioenergy is one of the best solution for meeting Indonesia's current and future energy needs. Biogas is biomass-based bioenergy, which is the potential for future energy sources. Minimizing the environment's degradation is a significant aspect of preparing the biomass to biogas conversion model. Furthermore, the production of biogas with automatic monitoring and control will minimize new waste formation. Indonesian government regulatory support and total community participation will increase converting biomass into biogas as renewable energy into electrical energy. The paper analyzes the environmental impact of biomass conversion into biogas and proposed an environmentally friendly conversion model.

Design and Performance Simulation of Computer Control System for Automatic Monitoring of Upper Computer Communication Operation State

The upper computer communication operation state automatic monitoring system is mainly used to remotely monitor the equipment, obtain various parameter indexes in the operation process of remote equipment, realize remote monitoring and fault diagnosis, and improve the management efficiency of decentralized equipment. This paper completes the design of communication, data storage, query, and other subsystems of upper and lower computers. The lower computer establishes a data channel with the OPC server through the MPI protocol and uploads the collected data to the OPC server in real time. The upper computer reads the data through the OPC server and displays the changes of monitored parameters in real time through the monitoring interface, so as to give an alarm under abnormal conditions. In addition, since the default database of Kingview is access, considering that the Microsoft Access database can store up to 2G of content, in order to upgrade and expand the subsequent system, SQL Server database is selected for data query, backup, and saving. The parameter setting method of communication control system is analyzed, the simulation model of industrial boiler control system is established by using Matlab/Simulink, and the interface between host computer software (IBCCS-e) and the model is provided. This paper analyzes the results of communication parameter adjustment. The simulation results show that the industrial boiler computer control system (IBCCS) has stable performance, low cost, convenient operation, and good maintainability. After further improvement, it has certain application value in the operation transformation of new small- and medium-sized boilers and original boilers.

Automatic monitoring system of Apis cerana based on image processing

The flight behavior of honey bee Apis cerana is influenced by environmental conditions. The observation of the number of bees flying in and out from the hives is needed to detect the Colony Collapse Disorder (CCD) phenomena. In this research, we build a prototype of an automatic monitoring system based on image processing. This instrument is intended to automatically monitor and count the number of in and out activities of A. cerana forager bees. This monitoring system detects the red, green, blue, and yellow marked bees by using a camera module of Raspbery Pi mini-computer which is programmed in Python language (and assisted by OpenCV library). The monitoring system is also equipped with temperature, humidity, and light intensity sensors to accurately describe the environmental condition during the measurement. The results show that the highest number of flight activities occurred around 8:00.-09:00 am, then decrease to noon and increased again at 1:00 pm - 3:00 pm.

Online Transformer Monitoring System

Power transformers, are basically used for stepping up& down the voltage levels. They are the primary equipment used in power transmission system. So it is primary to maintain all the transformers located geographically, but due to lack of man power it is impossible to monitor regularly. Due to these reasons, if a failure occurs in transformer may cause the network power shutdown. Though there are lot of protection measures that accompany a transformer, but by providing a online monitoring system will increase the reliability and reporting instantaneous fault confidently. This paper gives out the details in design and construction of an automatic monitoring system for power transformer parameters. A node-mcu module [esp8266] was enabled to monitoring of voltage, oil level and temperature (oil & winding) on a typical power transformer. With the internet of things (IoT), a self-defense system is designed and implemented for the transformer. In this system Transformer parameter are continuously marked and a graph is plotted. If the level of the parameter increases than the actual value, it gives buzzer alarm, if no action taken then the whole system will be tripped safely in power transformers.