The recent advancements in Computer Vision technology are upgrading the overview of its applications in agriculture. The combination of Artificial Intelligence, Computer Vision, and Machine Vision is making the world of farming more advanced than humankind ever discovered. Employing Agri-Tech is disrupting the traditional dynamics of farming by helping farmers in better crop yielding. Using the framing robots to plant diagnosis applications, Computer Vision and Deep Learning models bring tremendous change. As the prominence of Computer Vision in agriculture continues to grow, the Zion market research has estimated that this technology can provide a sustainable future for farming.

Today we are accessible to a mountain of intelligent technologies. And no doubt that computer vision stores a vital space among all of them. When we talk about computer vision, the foremost application that we think of is Image Recognition. But indeed, a computer vision also encompasses OCR (Optical Character Recognition) algorithm, which allows seamless computer operations. In this article, we will discuss the origin, advancements, OCR tasks, and OCR industry applications that are enriching the OCR Pipeline.

APIs (or Application Programming Interfaces) have been identified as important intermediaries between technologies like machine learning(ML) and their end-users. With big data streaming in vast data pools, organizations are turning towards machine learning APIs to leverage the technology and withdraw the complexities involved in creating and deploying machine learning models. APIs are making machine learning more consumable, scalable, and programmable. After machine learning's separation from statistics in the 1980s, the focus shifted towards inventing new algorithms and research on parameter estimation, scalability, and automation to establish it as a new technological advancement. But the main challenge was the fact that the development, usage, and implementation of the machine learning models were done by only tech geeks with domain knowledge.

Image Segmentation is considered a vital task in Computer Vision – along with Object Detection – as it involves understanding what is given in the image at a pixel level. It provides a comprehensive description that includes the information of the object, category, position, and shape of the given image. There are various algorithms for Image Segmentation that have been developed with applications such as scene understanding, medical image analysis, robotics, augmented reality, video surveillance, etc. The advent of Deep Learning in Computer Vision has diversified the capabilities of the existing algorithms and paved the way for new algorithms for pixel-level labeling problems such as Semantic Segmentation. These algorithms learn rich representations for the problem, including automatic pixel labeling of images in an end-to-end fashion.

Deep convolutional neural network (CNN) based image classification plays an essential role in seamlessly performing most of the challenges from disease diagnosis to predicting consumerism behavior. Using Deep CNN reduces the time and effort required to spend on extracting and selecting classification features manually. In recent times, deep CNN has been applied to image classification … ...

Influencer marketing has grown significantly in the past few years. Due to the pervasive use of social media channels to promote products and services, influencer marketing can be considered as a new-age digital revolution. With a digitally empowered general populace, the CPG industry is now able to authenticate and communicate brand stories across distinct socials to earn better traction and win markets' advantage. Influencer marketing is experiencing an escalated growth. It is projected that the global market for influencer marketing will reach $15 billion by 2022.

Smart cities are a new-age revolution to maximize the utilization of technology, optimize the consumption of natural resources, and human capital to fuel sustainable economic growth and participatory governance. Smart cities are mostly employed across highly populated urban areas in major countries. For instance, these cities adopt a combination of cameras, sensors, and artificial intelligence to ensure constant monitoring along with their smooth and efficient working. This results in the emergence and adoption of smart business models and smart enterprises that depend on advanced technologies like artificial intelligence(AI), machine learning, computer vision, web technologies, telecommunications, etc. Markets and Markets expect that the market for global smart cities will grow from USD 410.8 billion in 2020 to USD 820.7 billion by 2025.

Explore machine learning with DeepLobe, a unique MLaaS API for applications like image recognition, OCR, object detection, etc.