Agriculture – the food generating sector is one of the leading occupations among the people in rural areas lacks due to underdeveloped methodologies or use of outdated know-how. But now AI in agriculture is boosting this sector using the power computer vision technology, to train the machines for better productivity in agro and farming.
Actually, high-labor cost and unavailability of such manual labor or increasing aesthetic standards for agricultural products, and greater global competition, encouraging farmers to adopt the latest automation technology to minimize their cost of production and improve the crop yield with better efficiency and margins in the markets.
AI companies can utilize the computer vision technology used in machine learning or deep learning in AI that can only help machines to recognize the various aspects of agricultural production and help farmers for precise farming.
In respect of the same, we brought here a great discussion what is the automated system, or how AI-based applications or machines can be trained and used to create a computer vision-based AI model for agriculture and farming. And you can also find how AI companies can create the training data sets to train such models for this field.
Application of Computer Vision in Agriculture
In agriculture, a well-trained Robots can be used for performing various tasks like planting, weeding, harvesting and plant health detection. Such robots can detect plants, weeds and fruits or vegetables with the power of analyzing the health condition and fructify level to determine the harvesting time with the reaping capability of such crops.
To train the computer vision-based AI model, annotated data in the format of images or pictures are used to make the subject or object of interest recognizable to machines through machine learning algorithms for similar predictions.
And there are multiple techniques to annotate the images for robotics used in agriculture and farming. To detect the crops, fruits and vegetables bounding box annotation is used to make these plants recognizable to machines.
Bounding box annotation can be used by AI companies to detect the plants, check the fructification level and recognize the unwanted plants or weeds. Bounding box annotation provides the right inputs to computer vision for plant detection.
Computer Vision in Drones for Crop Monitoring
Drones are playing a crucial role in precise agriculture and farming. While flying in the midair, this autonomous flying object can capture a huge amount of data through a camera installed for computer vision detection and training.
Drones can get the ariel view of the entire field or cultivated ground and create a 3D map imagery that can be viewed on a computer screen from distance to monitor the health of crops or check soil conditions through geosensing and visual sensing.
Video: Computer Vision in Crop Monitoring through Drones
So, right here apart from the bounding box, semantic segmentation image annotation and polygon annotation techniques are used to train the drones for mapping the agricultural fields and analyze the data for the right forecasting.
Computer Vision in Drones for Livestock Management
Similarly, semantic segmentation is also used to make the animals recognizable from the midair making the AI possible in livestock management. A well-trained drone can recognize livestock, count them and monitor them without human’s help.
Image annotations like the bounding box technique also help to detect and recognize livestock helping animal husbandry businesses operate with more efficiency for better productivity. In farming using the right algorithms, computer vision-based models are trained to detect the different types of animals without the help of humans.
Computer Vision for Yield Prediction Using Deep Learning
Apart from automated machines, the AI in agriculture can help by predicting the crop yield using deep learning technology. Actually, deep learning with the help of satellite imagery, various information can be gathered like soil conditions, nitrogen levels, moisture, seasonal weather and historical yield information of crops for precise farming.
And, using the deep learning technology AI software or application can be trained to analyze such things and that can be used on smartphones or tablets using the computer vision through the device camera to analyze the crops.
Computer Vision in Forestry Management
Computer vision technology is also used in autonomous machines like drones to analyze the aerial images of trees taken from heights, or by plane or satellite to monitor the deforestation activities and monitor the health condition of trees.
In forestry huge amounts of data are used to train the AI model to produce accurate measures, assessing the health and the growth of trees and enabling forest management professionals to make more accurate decisions.
Computer Vision in Drones for Spraying Pesticides on Crops
The AI-enabled drones are capable to monitor the infected crops and spray the pesticides to prevent crops from insects and pests. The computer vision allows drones to precisely detect the infected crops and spray the pesticides accordingly. And further with more improved vision power of a computer, more precision will protect crops.
Video: Drones Spraying Pesticides on Crops:
Computer Vision in Grading and Sorting of Crops
AI in computer vision for agriculture and farming can be also used to sort good crops from bad crops and determine which will be stable for longer shipments and which will go bad first and should be shipped to local markets.
Using the deep learning techniques once the percentage of infection is calculated then on the basis of percentage do the grading and sorting of the fruit image helping farmers to reduce the crop damages due to storage.
The right application of computer vision in agriculture is possible when the AI model is well-trained with annotated training data to make the varied objects or interest recognizable o machines. Anolytics is providing the image annotation services for computer vision-based machine learning or deep learning model training.
Video: Sorting of Fruit using Machine Learning
So, if you are looking to develop a computer vision-based AI model for agriculture and farming get in touch with Anolytics that can provide you the best quality of data sets at a most affordable price while ensuring the accuracy at each stage.
Anolytics can annotate the images for varied AI models used in agriculture and farming. From robotics to autonomous flying objects like drones, it can create high-quality training data sets for computer vision in precise farming. It is working with well-trained annotators to annotate the images with best quality for accurate recognition by machines for the right predictions.
How AI is Used in Healthcare to Control the Coronavirus Disease?
Coronavirus or scientific name COVID-19, is one the most deadly virus of the century infected around 10 million people and killed tens of thousands of folks globally. Being declared a pandemic by WHO, this infection is spreading among the people at a very fast speed, bringing down the mass population at the hospital bed affecting the world economy.
As, coronavirus is a highly contagious disease, worldwide Governments have lockdown the cities to stop or minimize the further spread of infection due to community transmission. However, this kind of approach is also affecting the economic activities, impacting the global economy due to halted productions of goods and services.
Actually, the reason behind lockdown is that, there is no medicine or vaccine specifically developed to treat or cure the coronavirus infected patients. People who are infected with coronavirus infection in critical conditions are killed to death.
Though, medical researchers and scientists are endeavoring day-night with their best efforts to develop the medicine, but until such medicine or vaccine get developed to control this infection spreading among more humans is the best option to minimize its post-impact on the whole world in terms of causalities, social changes or economic growth.
AI in Healthcare
However, meanwhile, countries are using the cutting-edge and most advance technologies into the healthcare system to combat the coronavirus. And AI in healthcare is playing a vital role in fighting with this disease and assist medical staff to minimize their efforts and help patients to recover soon without risk of community transfer.
Though, Machine Learning (ML) and Artificial Intelligence (AI) based systems, machines and models are being used in hospitals, medical centers and healthcare organizations to track the activities of patients, monitor their health, assist them physically or give the useful information of patients to the doctors to provide the right treatments and care.
Actually, there are many AI-enabled devices, machines, systems and applications can be used to deal during the treatment and care of patients fighting with such deadly disease. So, right here we will discuss how big data and AI is used in the healthcare system to combat the coronavirus outbreak in the different part of the countries globally.
AI Robots for Food Deliveries & Disinfection
Robotics technology is not new to the world, but AI-enabled robots are playing a crucial role in assisting medical staff or helping patients. It is used to deliver food, spray disinfectants and performed basic diagnostic functions, in order to minimize the risk of cross-infection, which is the most dangerous part of coronavirus transmission.
In various hospitals, robots are used to diagnose and conduct thermal imaging to check body temperature and other symptoms in humans. In China, Shenzhen-based Pudu Technology, which usually makes robots for the catering industry, installed its machines in more than 40 hospitals all over the country to help medical staff.
AI Drones for Monitoring & Medical Supplies
Similarly, AI in drones developed with the help of machine learning technology to train such autonomous flying objects to perform various actions at places where humans can’t reach easily or need extra time or effort to complete the action.
Actually, AI-enabled drones are equipped with multiple advance features like a high-resolution camera with object and face detection technology or navigate automatically while controlled by the people distantly to deliver goods easily.
Amid coronavirus control, Drones are also flying with QR code placardsthat can be scanned to register health information. While agriculture dronesthat are used for spraying pesticides are now used to spray disinfectants inthe countryside.
Furthermore, during the lockdown, face recognition enabled Drones are also being used to alert or announce warnings to the citizens to not come out of their homes and also advise them to wear a mask and maintain the social distancing.
In China, drones are used to alert people while creating a kind of automated surveillance in infected areas. The drones are also being used to inform people about the areas that could be potentially infected, thanks to the integration of AI in drones.
Autonomous Vehicles to Transport Goods
Though, self-driving cars are not full-fledge in use, but AI-enabled driverless autonomous vehicles are used to deliver essential products. To avoid human contacts at coronavirus hospitals and healthcare centers, autonomous vehicles are proving to be of great utility in delivering essential goods like medicines supplies and various foodstuffs.
These driverless vehicles are trained and developed with AI-based technology to detect objects and visualize their surroundings through computer vision and sensor technology to move in the right direction without any collision.
Watch Video: How AI Robots & Drones are used in Coronavirus Control
And some of the autonomous vehicles are also used in China is used to disinfect hospitals. Apollo, a Baidu’s autonomous vehicle platform, working jointly with self-driving Car Company to deliver supplies and food to a big hospital.
Facial Recognition System to Detect Humans
Using computer vision technology, the face recognition system is utilized to detect humans. And AI-enabled security cameras is making security and surveillance system more effective to monitor the human activities around the cities.
Meanwhile, AI companies using big data are also developing a dashboard for face recognition and infrared temperature detection in all the leading cities where mass gathering or movement of people happens every day.
Such AI security camera system also helping healthcare authorities and security agencies to monitor the people quarantined step outside during lockdown. It can detect people wandering in groups or not wearing the facemasks.
Watch Video: In China How Face Recognition is used to Identify People Wearing Mask
Shared Big Data for Analysis & Predictions
The AI and ML-based models are developed with a huge amount of data sets from that particular field. Similarly, in the case of coronavirus outbreak control and treatment, the big data is analyzed to predict the further spread of such a virus.
Social media giant, Facebook is working with researchers at Harvard University’s School of Public Health and the National Tsing Hua University, in Taiwan, sharing anonymized data about people’s movements and high-resolution population density maps, which help them forecast the spread of the virus in other parts of the different nations.
This social media networking website is also helping partners understand how people are talking about the issue online, via tools to aggregate social media posts talking about such an epidemic. Previously, Google search data has been used to track infectious diseases helping healthcare authorities to take preventive measures timely.
Similarly, Smartphone apps are also being used to keep a tab on people’s movements and ascertain whether or not they have been in contact with an infected person. A leading Chinese telecom company China Mobile using to send text messages to state media agencies, informing them about the people who have been infected in the country.
Helping to Discover the Drugs for COVID-19
AI is not only integrated into machines through machine learning but playing a great role in discovering the right drug for COVID-19 like new diseases.
Actually, in drug medicine, AI can help in various ways to combat this deadly disease. AI is helping medical researchers and doctors to rapidly develop antibiotics and vaccines for the COVID-19 virus, scan through existing drugs to see if any could be repurposed and design a drug to fight both the current and future coronavirus outbreaks.
Even, an AI-driven platform for drug discovery has identified nine potential drugs that can relief against COVID-19 and six of them are already approved in many countries and used by doctors to cure patients helping them to recover.
It can identify molecules with potential effects on the coronavirus replication. The fact that this time the potential treatments were found among existing drugs marks a significant improvement over previous efforts to use AI against COVID-19.
Meanwhile, Google-owned AI Company, DeepMind, has used its AlphaFold system to release structure predictions of several proteins associated with the virus. AI is also helping to develop the vaccine which could take 18 to 24 months.
Doctors using the medical images datasets of coronavirus infected person to understand the complexities of infection and analyze the epidemiologic characteristics, clinical manifestations, chest images, and laboratory findings. And the diagnosis of 2019-nCoV pneumonia can be used to train the AI models to detect similar symptoms among patients.
Overall, AI integrated into the various systems, machines and devices are making the healthcare system more automated with an acceptable level of accuracy. And if machine learning engineers and data scientists use more quality healthcare training data to develop such AI models, it would become easier to predict or combat with such deadly diseases.
Companies are providing the healthcare training data in the form of annotated images of radiology scans like MRI, CT Scans that are used to train the computer vision-based AI models. They can annotate all medical imaging to provide algorithm training datasets using various popular image annotation techniques like semantic segmentation and polygon image annotation for organ segmentation and disease diagnosis with the best level of accuracy.
Why Vaccine Development Process for New Diseases Like Coronavirus Takes 12 to 18 Months?
Amid the new coronavirus (COVID-19) outbreak, many people especially, common man is thinking why medical scientist and healthcare researchers are taking too much time to develop the vaccine so, that people can be immune to protect from this disease.
Though, the clinical trial for new coronavirus is already under process globally, but the process of producing a usable coronavirus vaccine will take at least 12 to 18 months before make it available to the public at mass level for vaccination.
Coronavirus Vaccine Development Process
Actually, the process of testing and approving a new treatment or vaccine is a complex one, and for good reason. There need to be safeguards in place to confirm that a treatment or vaccine is effective and safe before using it on the general public. Hence, we brought here the vaccine clinical trial and development process.
CURRENT PHASE: RESEARCH & DISCOVERY
Currently, companies and labs around the world are working to identify and develop potential vaccine candidates and test them on animals to determine whether they’re promising and safe enough for human trials.
Once the vaccine has been proven to be successful and safe in animal testing, the FDA will approve it for human clinical trials. Some companies are already pushing to skip animal testing to accelerate the timeline to get to release, but many researchers are concerned about the risks of skipping this crucial step, let see the phases.
The first phase of the human clinical trial will involve giving the potential vaccine to a small group of healthy people (fewer than 100) and then monitoring those people for a few months for effectiveness and side effects.
This will help researchers understand the effect of the vaccine on the human body and whether there are harmful side effects that outweigh the benefits of the vaccine.
Once the vaccine passes the first phase, it will be approved for phase 2, which will involve a larger group of patients monitored over a longer period of time. Phase 2 clinical trials usually involve hundreds of patients and can take up to two years.
Due to the immediate nature of the pandemic and the fact that the vaccine is meant for the general population (and not a very specific subset of the population that could take a long time to recruit) this phase could be completed much faster than the normal timeframe.
Once the vaccine candidate is proven successful in the phase 2 trial, it will be approved for phase 3, in which the vaccine will be given to thousands of patients to gauge varying effectiveness and safety with different subsets of the population.
This will help ensure that the vaccine is truly effective for everyone and not just healthy and young members of the population.
FINAL PHASE: APPROVAL & RELEASE
Finally, the FDA will review the results of the clinical trials and approve the vaccine for the public. Then the company behind the vaccine can mass-produce the vaccine for public use and help them get immune with coronavirus infection.
The vaccine creator will still be required to run phase 4 trials after release to look for additional side effects and understand the long term effects of the vaccine once it’s out on the market. And at any time the vaccine can be taken off the market if necessary.
Even with an immediate global need, unlimited funding (it will take billions to shepherd vaccine candidates through the testing and approvals process), and collaboration from dozens of companies and governments, it will still take over a year before any vaccine will be available to control the COVID-19
Meanwhile, Australian researchers said they have mapped the immune responses from one of country’s first coronavirus patients, findings the health minister said were an important step in developing a vaccine and treatment.
Watch Video: The Race To Develop A Coronavirus Vaccine
While on the other hand, US volunteers test first vaccine for coronavirus and first human trial of a vaccine to protect against pandemic coronavirus has started in the US. But the biotechnology company behind the work, Moderna Therapeutics, says the vaccine has been made using a tried and tested process with more research.
Till now, none of vaccine is developed and not available in the market, you need to be very careful and stay protected from this virus with right preventive measures guided by designated medical authorities or health organizations.
Source: Deep6 AI
What Is The Use And Purpose Of Video Annotation In Deep Learning?
Just like image annotation, video annotation also helps machines to recognize the objects through computer vision. Basically, the main motive of video annotation is detecting the moving objects in the videos and makes it recognizable with frame-to-frame outlining of objects to train the AI models developed with deep learning.
Use of Video Annotation
Apart from, detecting and recognizing the objects, which are also possible through image annotation, there are various reasons video annotation is used in creating the training data set for visual perception based AI models observe varied objects.
Actually, these models get trained through an algorithm to perceive the various types of objects through video annotation service. So, right here, apart from object detection, we will explain what is the use and purpose of video annotation in deep learning.
Frame-by-Frame Objects Detection
The first and most use and purpose of video annotation is capturing the object of interest frame-by-frame and making it recognizable to machines. The moving objects run on the screen annotated using the special tool for precise detection through machine learning algorithms used to train the visual perception based AI models.
Object Localization for Computer Vision
Another use of video annotation is localizing the objects in the video. Actually, there are multiple objects visible in a video and localization helps to locate the main object in an image, means the object mostly visible and focused in the frame. Actually, the main task of object localization is to predict the object in an image with its boundaries.
Object Tracking for Autonomous Vehicle
Another important use of video annotation is help visual perception AI model build for autonomous vehicle is after detecting and recognizing the objects track the varied category of objects like pedestrians, street lights, sign boards, traffic lanes, signals, cyclists and vehicles moving on the road while self-driving cars is running on the street.
Tracking the Human Activity and Poses
Another significant purpose of video annotation is again to train the computer vision based AI or machine learning model track the human activities and estimate the poses. This is mainly done in sports fields to track the actions athletes perform during the competitions and sports events helping machines to estimates the human poses.
These are various use of video annotation, and all these are done for the computer vision to train the visual perception based model through machine learning algorithms. In self-driving cars and autonomous flying drones, video annotation is mainly used to train the model for precise detection, recognition and localization of varied objects.
There are many video annotation companies providing the data labeling service for AI and machine learning. If you need a video annotation for deep learning, you can get in touch with Anolytics, that offers a world-class video annotation service to annotate the object of interest with frame-by-frame annotation at best level of accuracy.
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