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Application of Computer Vision in Precision Agriculture & Farming

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Application of Computer Vision Agriculture

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is one of the leading occupations among the people in rural areas lacks due to underdeveloped methodologies or use of outdated know-how.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.

Also Read: How AI Based Drone Works: Artificial Intelligence Drone Use Cases

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.  

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Artificial Intelligence

Top 5 Applications of Image Annotation in Machine Learning & AI

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applications of image annotation

At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous.

In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.

What is Image Annotation?

So, right here we will discuss the applications of the image annotation, but before we proceed, we need to review the definition of image annotation and its use in the AI industry. Image annotation is the process of making the object of interest detectable and recognizable to machines.

And to make such objects recognizable in the images, they are annotated with added metadata for the description of the object. And when a huge amount of similar data is feed into the model, it becomes trained enough to recognize the objects when new data is presented in real-life situations.

5 APPLICATIONS OF IMAGE ANNOTATION

Annotated images are mainly used to make the machine learn how to detect the different types of objects. But as per the AI model functions, ML algorithms compatibility and use in the various industries, image annotation applications also differ that all about we will discuss here below with the annotation types.

Detection of Object of Interest

The most important application of image annotation is detecting the objects in the images. In an image, there are multiple things, or you can say objects, but every object would be not required to get noticed by the machines. But the object of interest need to be get detected, and the image annotation technique is applied to annotate and make such objects detectable through computer vision technology.

Also Read: What Is Computer Vision In Machine Learning And AI: How It Works

Recognition of Types of Objects

After detecting the object, it is also important to recognize what types of objects it is, humans, animals or non-living objects like vehicles, street poles and other man-made objects visible in the natural environment. Here again image annotation helps to recognize the objects in the images.

Though, object detection and recognition runs simultaneously, and while annotating the objects in various cases, the notes or metadata is added to describe the attributes and nature of the object, so that machine can easily recognize such things and store the information for the future references.

Classification of Different Objects

It is not necessary all objects in an image belong to the same category, if a dog is visible with man, it needs to be classified or categorized to differentiate both of them. Classification of the objects in the images is another important application of image annotation used in machine learning training.

Also Read: The Main Purpose Of Image Annotations Is To Develop AI Model

Along with image classification, the localization of objects is also done through image annotation practice. In image annotation, there are multiple techniques, used to annotate the objects and classified into the different categories helping the visual perception based AI model detect and categorize the objects. 

Segmentation of Object in the Single Class

Just like object classification, objects in the single class need to be segmented to make it more clear about the object, its category, position and its attributes. Semantic segmentation image annotation is used to annotate the objects with each pixel in the image belongs to a single class.

The main applications of image annotation are to make the AI model or machine learning algorithm learn with more accuracy about objects in the images. For semantic segmentation, image annotation is basically applied for deep learning-based AI models to give precise results in various scenarios.

Recognizing the Humans Faces & Poses

AI cameras in smartphones or security surveillance are now able to recognize the face of humans. And do you how it became possible in AI world? Thanks to image annotation, that makes the humans face recognizable through computer vision with the ability to identify the person from the database and discriminate them among the huge crowd from the security surveillance system perspective.

In image annotation for face recognition algorithms, the faces of humans are annotated from one point to another point measuring the dimension of the face and its various points like chin, ears eyes, nose and mouth. And these facial landmarks are annotated and provided to the image classification system. Hence, image annotation is playing another important role in recognizing the people from their faces.

TYPES OF IMAGE ANNOTATION    

I hope you got to know the applications of image annotation in the world of AI and machine learning. Now you should know what are the types of image annotations used to create the machine learning training datasets for deep learning-based AI models? And we will also discuss here the application of different types of image annotation into various industries, fields and sectors with uses cases of AI-based models.   

Bounding Box Annotation to Easily Detect the Objects

Bounding box annotation is one of the most popular techniques used to detect the objects in the images. The object of interest are annotated either in a rectangular or square shape to make the object recognizable to machines through computer vision. All types of AI models like self-driving cars, robots, autonomous flying objects and AI security cameras relying on data created by bounding box annotation.   

Semantic Segmentation to Localize Objects in Single Class 

To recognize, classify and segment the objects in the single class, semantic image segmentation is used to annotate the objects for more accurate detection by machines. It is actually, the process of diving the images into multiple segments of an object having the different semantic definitions. Autonomous vehicles and drones, need such training data to improve the performance of the AI model.    

3D Point Cloud Annotation to Detect the Minor Objects

The image annotation applications not only include object detection or recognition, but even can also measure or estimate the types and dimensions of the object. 3D point cloud annotation is the technique that helps to make such objects detectable to machines through computer vision. Self-driving cars are the use case, where training data sets are created through 3D point cloud annotation. This image annotation helps to detect the object with additional attributes including lane and sideways path detection.

Landmark Annotation to Detect Human Faces & Gestures

Landmark annotation is another type of image annotation technique used to detect human faces. AI models like AI cameras in security surveillance, smartphones and other devices can detect the human faces and recognize the gestures and various human possess. Landmarking is also used in sports analytics to analyze the human possess performed while playing outdoor games. Cogito provides the landmark point annotation with the next-level of accuracy for precise detection of human faces or their poses.  

3D Cuboid Annotation to Detect the Object with Dimension

Detecting the dimensions of the object is also important for AI models to get a more accurate measurement of various objects. The 2D images are annotated with capturing all the dimensions visible in the image to build a ground truth dataset for 3D perception on the objects of interest. Again autonomous vehicles, AI robots and visual perception models used to detect the indoor objects like carton boxes with the dimension need such annotated images, created through 3D cuboid annotation.  

Polygon Annotation to Detect Asymmetrical Shaped Objects 

Similarly, polygon annotation is used to annotate the objects that are in irregular shapes. Coarse or asymmetrical objects can be made recognizable through the polygon image annotation technique. Mainly road marking or other objects are annotated for the self-driving cars. And autonomous flying objects like drones, viewing the objects from Ariel view can detect or recognize such things when trained with training data sets created through polygon annotation for precise object detection.

Polyline/Splines/Line Annotation for Lane or Path Detection

Lines, Polylines and Splines are all similar types of image annotations used to create the training data sets allowing computer vision systems to consider the divisions between important regions of an image. The boundaries, annotating lines or splines are useful to detect lanes for self-driving cars. Road surface marking that are indicating the instructions of driving on the road need to also make understandable to autonomous cars. Polyline annotation that divides one region from another region.

The right applications of image annotation are possible when you use the right tools and techniques to create high-quality training data sets for machine learning. And Cogito is the industry leader in human-powered image annotation services with the best level of accuracy for different AI models or use cases. Working with a team of well-trained and experienced annotators, it can produce the machine learning training data sets for healthcare, agriculture, retail, automotive, drones and robotics.

This article was originally written for cogitotech.com

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Artificial Intelligence

Types of Medical Diagnostic Imaging Analysis by Deep Learning AI

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types of medical diagnostic imaging

Artificial intelligence (AI) and machine learning have enough potential to make various tasks in the healthcare industry possible with accurate performance. Patient’s timely disease diagnosis and the right decision is an important part of hospitals to improve the overall healthcare system.

Medical imaging is playing a vital role in diagnosing the various types of diseases among patients across the healthcare system. MRI, CT Scan, Ultrasound and X-Rays are the key medical images use to diagnosis the disease, which is usually performed manually by the specialist doctors called Radiologists.

But now, Artificial intelligence in medical diagnosis is helping radiologists to use such images and detect the maladies with an acceptable level of accuracy. And now AI is capable enough to detect the different types of critical diseases with more level of accuracy.

To make the AI model, learn and detect, similar medical images are used that are annotated by the experienced radiologist to make the affected areas recognizable to machines through computer vision. So, today we will discuss here types of medical diagnostic imaging used to train the machine learning models and what are the data annotation techniques to create such medical imaging training data for AI.

Types of Diseases Diagnosed with AI in Medical Imaging

Detecting the Neurological Abnormalities

AI in neuroimaging is now detecting brain-related injuries, blood clotting and other neurological disorders with a high level of accuracy. A team of Radiologists annotate and highlight such brain-related disorders and helping the algorithms learn from the same.

AI in neuroimaging

Once the machine learning algorithms, get fully trained to diagnose and predict the brain-related problems, it can be used on the place of radiologist making the medical imaging diagnosis process faster and more correct for timely detection of brain diseases.      

Also Read: How AI in Medical Imaging Can Help in Diagnosis of Coronavirus

Semantic segmentation and polygon annotation are the leading techniques used to label the medical imaging training data for brain-related abnormalities.   

Screening of the Common Cancers

Cancer – one of the most common and deadly diseases, people are fighting to survive globally. Cancer is more life-threatening if diagnosed at last or critical stage of development. At this level, it becomes difficult for the oncologist to save the patients from dying.

ai in breast cancer diagnois

Also Read: How Does Google AI Detect Breast Cancer Better Than Radiologists

Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively taking their lives due to lack of timely detection. In breast cancer, microcalcification is the key tissue sometimes difficult to conclusively identify as it could be either malignant or benign.

ai in prostate cancer diagnosis

And if the diagnosis was false positives it could lead to unnecessary invasive testing or treatment, while missed malignancies could result in delayed diagnoses and worse outcomes for the patients. Similarly, AI deep learning” can detect the prostate and other types of cancers with accurate results. 

Also Read: How Does AI Detect Cancer in Lung Skin Prostate Breast and Ovary

Diagnosing the Infections in Kidney & Liver

AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. In liver medical imaging, physicians usually detect, characterize, and monitor diseases by assessing liver medical images visually.

ai in kidney liver diagnosis

However, sometimes, such visual assessment, done by experts or experienced radiologist doctors, may be biased due to personal experiences and inaccuracy. While on the other hand, AI can make a quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.

Whereas, AI applications in kidney disease are currently no significant, but the potential of AI in the management of kidney disease is well recognized by clinicians, hence the role of AI in kidney disease diagnosis will improve the diagnosis process in the future.

Also Read: How AI Robotics is Used in Healthcare: Types of Medical Robotics

Acute kidney problems like severe infections and stones can be detected with an AI-enabled medical imaging process. Basically, the function of AI in kidney disease mainly focuses on four aspects: Alerting systems, Diagnostic assistance, Guiding treatment and evaluating prognosis.

And with well-organized and annotated training a machine learning models can predict the kidney failure possibilities among the patients suffering from kidney-related problems. To detect the kidney stones, bounding box annotation is used to make such ailments recognizable to machines.

Brain Tumor Detection with High Accuracy

Just like cancers, tumors are also life-threatening disease, especially if it is developed in the brain. And AI in brain tumor detection is also possible with a precise medical imaging technique. As per the various research and studies, in brain tumor detection, the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation.

AI in brain tumor detection

Semantic segmentation technique is used to create such medical imaging data used for machine learning for brain tumor diagnosis. And as per the new studies, a novel method of combining advanced optical imaging with an artificial intelligence algorithm produces an accurate, real-time intraoperative diagnosis of brain tumors.

Machine Learning for AI in Dental Imaging Analysis

AI in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping doctors to recommend the right treatment. Using the dental image analysis, AI models can detect the teeth related problems including damaged teeth, uneven teeth structure or cavities and other maladies inside the roots of the teeth.

dental image analysis

Also Read: How Machine Learning In Dentistry Can Improve The Dental Imaging Analysis

In dentistry, the affected or damaged teeth conditions are outlined in the X-Ray images by an experienced radiologist, once such data sets get ready, it is used in ML. The ML algorithm learns from varied types of annotated dental x-rays, and learn from such source data, that is further used to detect when shown such x-rays and predict the teeth condition.       

Semantic segmentation is one of them used for detection, classification and segmentation of objects (teeth) in dentistry. Highly experienced dentist cum radiologist, examine the medical images or x-rays and annotate the affected areas with accuracy. Such a practice can create a huge amount of training data sets for machine learning in dentistry.     

Detecting the Bone Fractures and Musculoskeletal Injuries

The invisible bone fractures and other hidden musculoskeletal injuries can be fatal if undetected or untreated for a long time. Hip fractures and bone injuries in elderly persons are more critical due to reductions in mobility and associated hospitalizations.

AI in fracture detection

But medical imaging with machine learning, AI in fracture detection can diagnose the bone ruptures with the next-level of accuracy. X-ray imaging provides images of the body’s internal structures and when the fractured areas are annotated, it becomes delectable to machines through computer vision algorithm training with the huge amount of similar machine learning training data sets feed into the model.

Summing-up

AI in radiology is going to play a big role in diagnosing the various types of diseases including the critical maladies with a high level of accuracy. And further, with more improved or high-quality medical imaging data the diagnosis process and prediction accuracy will be better making the medical treatment and healthcare procedure more efficient and effective.

And Anolytics is one of the leading data annotation companies, providing the machine learning training data sets for AI developments in different industries like automotive, retail, agriculture and healthcare, etc. It is also offering a medical image dataset to train the AI models for diagnosing the various types of diseases with a high level of accuracy at every stage.

This article was originally written for anolytics.ai                 

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Machine Learning

What is Human in the Loop Machine Learning: Why & How Used in AI?

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In today’s era, mechanization taking place everywhere with a new age of development in more automated systems, applications, and robots, etc. Machine learning and AI are the leading cutting-edge technologies giving automation a new dimension with more tasks performed by machines itself.

Though, nowadays many tasks can be independently performed by AI-enabled devices, systems or machines without the help of humans. But developing such machines is not possible without the help of humans. So, Human-in-the-Loop or HITL is a model or concept require human interaction.

What is Human-in-the-Loop?

Human-in-the-loop (HITL), basically you can say, is the process of leveraging the power of the machine and human intelligence to create machine learning-based AI models. HITL describes the process when the machine or computer system is unable to solve a problem, needs human intervention like involving in both the training and testing stages of building an algorithm, for creating a continuous feedback loop allowing the algorithm to give every time better results.

What is Human-in-the-Loop

Humans annotate or label data then give to the machine learning algorithm to learn from such and take decisions from such predictions. And then humans also involve in tuning the model to improve its accuracy. And finally, these people test and validate the model by scoring its outputs, when machine learning algorithms not able to make the right decisions or gives incorrect decisions.      

Why Human-in-the-Loop Machine Learning is Used?

If you have a sufficient amount of datasets, an ML algorithm can easily make decisions with accuracy just learned from these datasets. But before that machine needs to get learn from the certain amount and quality of data sets, how to properly identify the right criteria and thus comes to the right results.       

Why Human-in-the-Loop Machine Learning

This where Human-in-the-Loop machine learning is used to the combination of human and machine intelligence creating a continuous circle where ML algorithms are trained, tested, tuned, and validated. In this loop, with the help of humans, the machine becomes smarter as well as more trained and confident to take the quick and accurate decisions when used in real-life and also help to train the algorithms.       

How Human-in-the-Loop Machine Learning is Used Today?

Human-in-the-loop is basically integrated through two machine learning algorithm processes – supervised and unsupervised learning. In supervised machine learning, labeled or annotated data sets are used by ML experts to train the algorithms, so that it can make the right predictions when used in real-life.

How Human-in-the-Loop Machine Learning

While on the other hand, in unsupervised machine learning there is no labels are given to the learning algorithm, leaving it on its own to find structure in its input and memorize the data in its own ways.

In HITL, initially, humans label the training data for the algorithm which is later fed into the algorithms to make the various scenarios understandable to machines. Later humans also check and evaluate the results or predictions for ML model validation and if results are inaccurate humans tune the algorithms or data is re-checked and again fed into the algorithm to make the right predictions.    

Why Human-In-The-Loop Computing is the Future Of Machine Learning?

Doing a machine learning process without human inputs is not possible. Algorithms cannot learn everything unless provided as per its compatibility. For example, a machine learning model cannot understand raw data unless humans explain and make it understandable to machines.

Human-In-The-Loop is the Future Of Machine Learning

Here, the data labeling process is the first step in creating a reliable model trained through algorithms, especially when data is available in an unstructured format. Actually, an algorithm cannot understand the unstructured data like texts, audio, video, images and other contents that are not properly labeled.

Also Read: The Main Objective of Image Annotation in Machine Learning & AI

Hence, the human-in-the-loop approach is required to make such data comprehensible to machines. These data are labeled as per the desired instructions like what is seen in the images, what is spoken in the audio or video using the data labeling or image annotation techniques to label such data.

When Human-in-the-loop Machine Learning is used?

Human-in-the-loop is not the concept you can implement in every machine learning project. Mainly HITL approach is used, when there is not much data available yet, human-in-the-loop is suitable because, at this stage, people can initially make much better judgments than machines are capable of.

When Human-in-the-loop Machine Learning

And using this, humans produce machine learning training data sets helping the machine to learn from such data. And human in the loop deep learning is used when humans and machine learning processes interact to solve one or more of the following scenarios:

  • Algorithms are not understanding the input.
  • When data input is interpreted incorrectly.
  • Algorithms don’t know how to perform the task.
  • To make humans more efficient and accurate.
  • To make the machine learning model more accurate.
  • When the cost of errors is too high in ML development.
  • When the data you’re looking for is rare or not available.

Human-in-the-Loop for Different Types of Data Labeling

As per the algorithms, different types of datasets in machine learning training are required. And the human-in-the-loop approach is used for such different types of data labeling process. If you want to train your model to identify or recognize the shape of objects like an animal on the road or other objects, then bounding box annotation is best suitable to make them recognizable to machines.

While, on the other hand, if you have to classify the objects in a single class, you have to use the semantic segmentation annotation suitable for computer vision to train the visual perception based ML model.

Similarly, to create facial recognition training data sets, landmark annotation is used. In language or voice-recognition machine learning training, text annotation, NLP annotation, audio annotation, and sentiment analysis is used to understand what humans are trying to say in different scenarios.

And when such data is labeled, annotated or make usable to machines, chatbot or virtual assistant like AI devices are developed to communicate with humans. Humans-in-the-loop can create different types of training data sets for different types of machine learning models built for different fields. 

AI is getting integrated almost every field around the world, but we still required Human-in-the-Loop, especially to produce and feed the training data into the algorithms at the initial stage of model development. Here, Cogito provides wide-ranging services for human-in-the-loop machine learning and human in the loop AI comprising text, videos, data and image annotation services for AI development.

This article was originally written for Cogito Tech

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