From sensor data to Munsell color system: Machine learning algorithm applied to tropical soil color classification via Nix™ Pro sensor

Geoderma ◽  
2020 ◽  
Vol 375 ◽  
pp. 114471 ◽  
Author(s):  
Marcelo Mancini ◽  
David C. Weindorf ◽  
Maria Eduarda Carvalho Monteiro ◽  
Álvaro José Gomes de Faria ◽  
Anita Fernanda dos Santos Teixeira ◽  
...  
Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3115 ◽  
Author(s):  
Yang Wei ◽  
Hao Wang ◽  
Kim Fung Tsang ◽  
Yucheng Liu ◽  
Chung Kit Wu ◽  
...  

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shazmina Gull ◽  
Imran Sarwar Bajwa ◽  
Waheed Anwar ◽  
Rubina Rashid

The modern age is an era of fast-growing technology, all thanks to the Internet of Things. The IoT becomes a prime factor of human life. As in this running world, no one cares about the wastage of food. However, this causes environment pollution as well as loss of many lives. A lot of researchers help in this era by introducing some great and beneficial projects. Our work is introducing a new approach by utilizing some low-cost sensors. In this work, Arduino UNO is used as a microcontroller. We use the eNose system that comprises MQ4 and MQ135 to detect gas emission from different food items, i.e., meat, rice, rice and meat, and bread. We collect our data from these food items. The MQ4 sensor detects the CH4 gas while the MQ135 sensor detects CO2 and NH3 in this system. We use a 5 kg strain gauge load cell sensor and HX711 A/D converter as a weight sensor to measure the weight of food being wasted. To ensure the accuracy and efficiency of our system, we first calibrate our sensors as per recommendations to run in the environment with the flow. We collect our data using cooked, uncooked, and rotten food items. To make this system a smart system, we use a machine learning algorithm to predict the food items on the basis of gas emission. The decision tree algorithm was used for training and testing purposes. We use 70 instances of each food item in the dataset. On the rule set, we implement this system working to measure the weight of food wastage and to predict the food item. The Arduino UNO board fetches the sensor data and sends it to the computer system for interpretation and analysis. Then, the machine learning algorithm works to predict the food item. At the end, we get our data of which food item is wasted in what amount in one day. We found 92.65% accuracy in our system. This system helps in reducing the amount of food wastage at home and restaurants as well by the daily report of food wastage in their computer system.


2021 ◽  
Author(s):  
Bogi Haryo Nugroho ◽  
Brahmantiyo Aji Sumarto ◽  
Muhammad Arief Joenaedy ◽  
Huda Jassim Al-Aradi ◽  
Pajar Rahman Achmad

Abstract Objective/scope It has been a challenge to analyze and estimate reliable water cut. The current well test data is not sufficient to satisfy the required information for prediction of the rate and water cut behaviors. Only on wells having stable and good behaviors, water cut levels can be estimated appropriately. The wells have Electrical Submersible Pump (ESP) sensor reading and data acquisition recorded in real-time help to fill this gap. The data are stored and available in KOC data repositories, such as Corporate Database, Well Surveillance Management System (WSMS), and Artificial Lift Management System (ALMS) Engineers spend this effort in spreadsheets and working with multiple data repositories. It is fit for data analysis by combining the data into a simple data set and presentation. Nevertheless, spreadsheets do not address a number of important tasks in a typical analyst's pipeline, and their design frequently complicates the analyses. It may take hours for single well analysis and days for multi-wells analysis and could be too late to plan and take preventive actions. Concerning the above situation, collaboration has been performed between NFD-North Kuwait and Information Management Team. In this first phase, this initiative is to design a conceptual integrated preventive system, which provide easy and quick tool to compute water cut estimation from well tests and downhole sensors data by using data science approach. Method, procedure, process There are 5 steps were applied in this initial work. It was included but not limited to user interview, exercise and performed data dissemination. It included gather full knowledge and defining the goal. Mapping pain points to solution also conducted to identify the technical challenge and find ways to overcome them. In the end of this stage, data and process review was conducted and applied for a given simple example to understand the requirements, demonstrate technical functionality and verify technical feasibility. Then conceptual design was built based on the requirements, features, and solutions gathered. Integrated system solution was recommended to include intermediate layer for integration, data retrieval, running calculation-heavy process in background, model optimization, visual analytics, decision-making, and automation. A roadmap with complete planning of different phases is then provided to achieve the objective. Results, observations, conclusions Process, functionalities, requirements, and finding have been examined and elaborated. The conceptual design has proved and assured the utilization of ESP sensor data in helping to estimate continuous well water cut's behavior. Further, the next implementation phase of data science expects an increase of confidence level of the results into higher degree. The design is promising to achieve the requirement to provide seamless, scalable, and easy to deploy automation capability tools for data analytic workflow with several major business benefits arising. Proposed solution includes combination of technologies, implementation services, and project management. The proposed technology components are distributed into 3 layers, source data, data science layer, and visual analytics layer. Furthermore, a roadmap of the project along with the recommendation for each phase has also been included. Novel/additive information Data Science for Exploration and Production is new area in which research and development will be required. Data science driven approach and application of digital transformation enables an integrated preventive system providing solution to compute water cut estimation from well tests and downhole sensors data. In the next larger scale of implementation, this system is expected to provide automated workflow supporting engineers in their daily tasks leveraging Data to Decision (D2D) approach. Machine learning is a data analytics technique that teaches computers to do what comes naturally to human, which is learn from experience. Machine learning algorithm use computational methods to learn information from the data without relying on predetermined equation as a model. Adding artificial intelligence and machine learning capability into the process requires knowledge on input data, the impact of data on the output, understanding of machine learning algorithm and building the model required to meet the expected output.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
M. Safdar Munir ◽  
Imran Sarwar Bajwa ◽  
Amna Ashraf ◽  
Waheed Anwar ◽  
Rubina Rashid

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data values. The decision from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm (KNN). Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge.


2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document