Machine learning and artificial intelligence solutions have propelled mobile app development to a new level. Applications with the integration of machine learning technologies can successfully recognize and classify images, human voices and actions, conduct text recognition from images and translate it. This list can easily go on. However, the main problem for engineers remains transferring huge models with millions of connections to a mobile phone, without losing processing speed and crucially — quality still remains.
Woodlands in Ukraine similar to most forests in other countries are at the risks associated with illegal deforestation. Official statistics of illegal logging in Ukraine show a decrease from 10,000 cases of illegal logging per year to 5,000 for the last 10 years period but independent observations indicate an increase in the number of violations. At the same time, according to state and civilian estimates, the amount of illegal logging is increasing and in 2019 amounted to more than 100,000 cubic meters. …
AgriTech is sprouting as an industry that attracts increasingly more investors. It aims to make farming easier, more data-oriented, ecological, and profitable. BIS Research predicts the global smart farming market to reach around $23 billion by 2022 and grow at a compound annual growth rate (CAGR) of almost 20% from 2017 to 2022. Such accelerated development is triggered by the increasing demand for crop yield and food production with the same resources. Technologies help agriculture businesspeople reinvent farming strategies and empower farmers with machine learning, IoT, big data analytics, and computer vision in agriculture.
Smart Farming Technologies as the Industry Driver
Also called the 3rd Green Revolution…
The amount of data that humanity generates is growing exponentially. IDC estimates that in 2021 only, about 75 trillion gigabytes, i.e., 75 zettabytes, of data will be created. With the help of data science, financial, surveillance, and social media companies, among others, analyze this information and derive additional benefits for their businesses for many years to come. Still, not all industries are equally good at adapting to and leveraging the power of data to drive their business.
The healthcare industry generates a massive amount of data that can be used by biotech companies to deliver advanced health tools to hospitals…
2020 has been full of challenges — the global pandemic and lockdown and a recession in the world economy are just some of the shocks faced by many businesses across all industries. Agriculture is no exception. According to FAO, the current crisis stopped farmers from completing sowing, closed borders restricted the movement of seasonal labor for harvesting, and reduced income of the world’s population has led to a decrease in purchasing power, including food.
In recent years, the role of data in agriculture has increased significantly. Advances in technologies such as big data, machine learning, cloud computing, blockchain, IoT, autonomous…
According to research, the global agriculture market is expected to grow 8% annually and reach $12 trillion in 2023. Due to this growth, the biggest challenges of the industry, like the world’s growing population, increasing urbanization, stressed usage of natural resources, and global climate changes, need to be handled. And one of the ways to meet the demand for food is the widespread use of data-driven technologies.
In April 2020, our team participated in the first Agriculture-Vision Challenge. The Challenge, organized by IEEE/CVF CVPR, was aimed to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images.
was aimed to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images.
Having extensive expertise and considerable experience in collecting and analyzing data from satellites, drones, and sensors for our customers within the agritech industry made us accept the challenge and try our hand at innovative pattern recognition.
If you’re serious about your machine learning experiments, you’ll need a tool to track and manage them. I personally prefer MLflow. It’s a platform that streamlines machine learning development by tracking experiments, packaging code into reproducible runs, sharing, and deploying models. It’s open-source, lightweight, and offers tons of integrations. That’s why I’d like to introduce you to the MLflow Tracking module and help you get it up and running in your data science project.
Precision agriculture is a step up from traditional farming. Also known as satellite farming, the concept is based on observing, measuring, and responding to inter and intra-field variability in crops. And variable rate application (VRA) is one of the most promising trends of modern precision agriculture. That’s why we’re so excited about it.
VRA is not just about fertilizing, seeding, and applying pesticides. For us, it’s more about the technologies used to automatically apply different kinds of expendable materials on a field, above and beneath it. …
We’re all used to smart home assistants now: they execute voice commands, alert us about suspicious activities, and tell jokes. Basically, many of us can buy it now — the price range is vast. But wouldn’t it be exciting to build one yourself? Well, not the entire assistant, but at least a custom audio processing system no smart assistant can function without.
In this article, we’ll give you a jump-start on how to create your own audio processing system. It includes steps to develop a system that uses an audio stream from a connected microphone and sends out alerts when…
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