Applied Technology Review : News

Artificial Intelligence (AI) in agriculture not only helping farmers to automate their farming but also shifts to precise cultivation for higher crop yield and better quality while using fewer resources. The agricultural industry turns to Artificial Intelligence (AI) technologies to assist farmers in producing healthier crops, controlling pests, monitoring soil and growing conditions, organising farmers' data, assisting with the workload, and improving a variety of farm-related tasks across the food supply chain. Below are some suitable Agricultural AI applications. The efficiency of weather forecasting: As climate conditions change and pollution levels rise, it becomes harder for farmers to determine the optimum seed sowing time. With AI, farmers can analyse weather conditions through weather forecasting, allowing them to plan the crop type that can be grown and when the seed can be sown. Soil and crop health monitoring system: Soil type and nutrition significantly impact crop type and crop quality. As deforestation increases, soil quality deteriorates, making it difficult to determine its quality. A German technology startup has developed an artificial intelligence-based application to detect nutrient deficiencies in soil and plant pests and diseases, giving farmers an idea of when to apply fertiliser to improve the harvest quality. This application uses image recognition technology. Farmer can use his smartphone to capture plant images. They can also view short videos on this application showing soil restoration techniques, tips, and other solutions. Similarly, a machine-learning-based startup helps farmers conduct soil analysis through their app to monitor soil and crop health to produce healthy crops with higher productivity. Drone-based crop health analysis:   Drone-based Ariel imaging solutions monitor crop health. The drone collects data from fields and then transfers it via a USB drive to a computer for expert analysis. It assists farmers in identifying pests and bacteria and implement pest control and other necessary measures promptly. Precision Farming and Predictive Analytics: AI applications in agriculture have developed applications and tools to help farmers inaccurate and control agriculture by providing farmers with adequate guidance on water management, crop rotation, timely harvesting, growing crop type, optimum planting, pest attacks, nutrition management. While using machine learning algorithms related to images captured by satellites and drones, AI-enabled technologies predict the weather, analyse crop sustainability and evaluate farms with data such as temperature, precipitation, wind speed, and solar radiation for the presence of diseases or pests and insufficient plant nutrition on farms. Farmers with no internet connectivity can now enjoy AI with tools as simple as an SMS-enabled phone and the Sowing App. Meanwhile, farmers can use AI applications to get an AI-customized plan for their land. With such IoT- and AI-driven solutions, farmers can meet the need for sustainably growing food production and income without depleting valuable natural resources. Agricultural Robotics: AI companies have developed robots to handle many agricultural jobs efficiently and control significant weeds and crop volumes faster. See Also:  Top Salesforce Solution Companies ...Read more

Neonode touch sensor modules have been chosen by major Asian airline to retrofit its contactless touch solutions featuring Neonode's technology.  Neonode Inc. is thrilled to announce that it has received an order for and will deliver touch sensor modules to Japan Aerospace Corporation, a Neonode value-added reseller, who has been chosen by a major Asian airline to retrofit its contactless touch solutions based on Neonode's technology on current self-service check-in and baggage drop kiosks at different airports across Asia. "This order represents a breakthrough in an important market segment for Neonode and we look forward to growing our contactless touch business with this and other customers in the transportation domain," said Dr Urban Forssell, CEO of Neonode. Neonode Inc., established in 2001 and headquartered in Stockholm, Sweden, is a publicly-traded company. The company provides innovative optical sensing systems for contactless communication, touch, gesture control, and in-cabin monitoring. Neonode's technology is currently used in over 75 million products, and the company holds more than 120 patents worldwide, based on expertise gained through years of advanced R&D and technology licencing. Some of the world's most well-known Fortune 500 companies in the consumer electronics, office supplies, medical, avionics, and automotive industries are among Neonode's customers. ...Read more

Companies that use digital twins can reap significant benefits such as improved operations, product and service innovation, and faster time-to-market. A digital twin is a virtual model that mimics the behavior of a physical object or process throughout its lifecycle. This technology allows you to remotely monitor and control equipment and systems by providing a near real-time bridge between the physical and digital worlds. Finally, it can run simulation models to test and forecast assets and process changes under various "what-if" scenarios. Creating a Digital Twin Necessitates Several Steps, Including: •  Sensors that record the operational behaviors of assets and processes (vibration, temperature, pressure, etc.) and their operating environments (air temperature, humidity, etc.) •  Communications networks that transfer data from physical devices to the digital world securely and dependably. •  A digital platform that acts as a modern data repository, gathering and storing shop floor sensor data alongside high-level business data (e.g., MES, ERP). By combining these data sources, actionable insights for data-driven decision-making can be derived – using advanced AI/machine learning algorithms. 3 Digital Twin Applications for Industry 4.0 Digital twin technology provides unprecedented visibility into assets and production, allowing for identifying bottlenecks, streamlining operations, and developing innovative products. The three major applications of digital twins for Industry 4.0 are listed below. Predictive Maintenance:  Spare part maintenance and replenishment can be planned to reduce time-to-service and avoid costly asset failures. Predictive maintenance using Digital Twins can provide OEMs with a new service-based revenue stream while improving product reliability. Process Planning and Optimization:  A digital footprint ingesting sensor and ERP data from a manufacturing line can greatly analyze important KPIs such as production rates and scrap counts. This aids in determining the root cause of any inefficiencies and throughput losses, optimizing yields, and reducing waste. Product Design and Virtual Prototyping:  Virtual models of in-use products provide detailed insights into usage patterns, degradation points, workload capacity, defects, etc. Designers and developers can correctly evaluate product usability and reliability by better understanding a product's characteristics and failure modes. The best way to start a Digital Twins initiative is to identify the asset(s) and processes with the greatest potential for value creation and then start with a pilot implementation. A digital twin should be a work-in-progress that evolves and scales in real-time as your IT capacity grows and matures. Digital twins of different components are typically interconnected to form a large, composite twin of a highly complex machine or process. ...Read more