Trending February 2024 # Top 5 Computer Vision Use Cases In Agriculture In 2023 # Suggested March 2024 # Top 7 Popular

You are reading the article Top 5 Computer Vision Use Cases In Agriculture In 2023 updated in February 2024 on the website Eastwest.edu.vn. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 Top 5 Computer Vision Use Cases In Agriculture In 2023

The agriculture sector is one of the most important industries in the world since it is the source of our food. As digital technologies revolutionize every industry, agriculture is no exception. Like every other sector, the agriculture sector also faces various challenges, including climate change, labor shortage, and the disruptions created by the pandemic.

Digital technologies such as computer vision can help the agricultural sector overcome these challenges and achieve efficiency, resiliency, and sustainability.

This article explores 5 computer vision use cases that can help agriculture tackle current challenges and excel in the future.

1. Crop monitoring with drones

Drone technology is being extensively used in the agriculture sector to overcome labor shortages and improve efficiency. The market for drones in agriculture is projected to reach $3.7 billion by 2027.

In precision agriculture crop monitoring, drones are installed with a high-definition camera which is enabled with computer vision and geothermal technology to:

Detect crop condition and health

Monitor soil condition

Map the farmland according to the crop area

Detect abnormalities

These drones can be highly efficient and can cover a large area much faster and more accurately than human monitoring.

Source: Business insider

However, investing in drones enabled with computer vision can be expensive; therefore it is important to study the business, short/long-term expectations, and ROI before purchasing such technologies.

2. Crop sorting and grading

Computer vision-enabled machines are being extensively used in sorting and grading the harvest. Since these jobs involve repetitive and time-consuming tasks, automating them can offer efficiency and speed.

Through machine vision systems, crops of different types can be identified and sorted based on order requirements. For example, some orders require large size potatoes, and some require medium-sized ones. A machine vision system can do this in a fraction of the time it would take to do it manually.

Machine vision systems can also sort products based on perishability to identify which batch to ship first and which ones to ship later.

Check out this computer vision-enabled apple grading machine.

Computer vision systems are also used in counting fruits and vegetables. Check out this example of a computer vision system counting apples directly from trees.

3. Pesticide spraying with drones

Spraying pesticides on crops is a common practice to protect the produce from pests and diseases. However, this can be a time-consuming process and if inhaled, it can be harmful to the farmer’s health. 

Automated drones can perform this task with higher precision and speed. Drones with spray guns and cameras enabled with computer vision can identify areas that need pesticide and spray accordingly in required amounts.

4. Computer vision phenotyping

Phenotyping refers to measuring and analyzing plants’ characteristics for research purposes. Information is gathered to learn how plants grow, what environment is best for specific plants, and insight into plant genetics. 

In the past, it was done manually, but now it is performed through AI and computer vision. As climate change threatens the agricultural sector, computer vision-enabled phenotyping enables breeders to learn more about plants to make them more resilient to the changing weather. It also helps farmers in finding the crop that would be most successful and sustainable. 

Watch this short video to learn how computer vision-enabled phenotyping works.

5. Livestock farming

Artificial intelligence is being widely used in the livestock farming market. The investment in AI is projected to significantly increase by 2026 and computer vision accounts for the largest chunk of that market.

Figure 1. Overview of AI livestock market increase from 2023 to 2026

Source: GMC

Computer vision technology combined with IoT can provide the following benefits for precision livestock farming:

Monitor the health of all livestock including cattle, livestock, sheep, pigs, and poultry 

Examine the health of the livestock with high definition cameras

Monitor food supply for the livestock

Detect abnormal behavior of the livestock

Counting livestock through drones

Send real-time information to the farmers for farm management planning and decision making

A recent study was conducted on a computer vision and deep learning system to monitor dairy cows with accuracy and with real-time data. The system successfully identified cows through pelt patterns, evaluated their position, understood the actions of the cows, and tracked movement.

Source: ScienceDirect

To ensure the success of your computer vision projects you can find the best annotation solutions from our sortable and filterable lists of:

Further reading

You have any questions, feel free to contact us:

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.

YOUR EMAIL ADDRESS WILL NOT BE PUBLISHED. REQUIRED FIELDS ARE MARKED

*

0 Comments

Comment

You're reading Top 5 Computer Vision Use Cases In Agriculture In 2023

Top 6 Use Cases Of Rpa In Change Management In 2023

According to Gartner, a typical, modern organization has implemented five major firm-wide changes in the last three years. And 75% of firms are expected to undertake even more changes in the next three years. 

But, per Gartner’s report, half of the change initiatives fail, with only 34% having a chance of “clear success.” 

According to Forrester, some possible reasons for unsuccessful change initiatives are the followings: 

What is change management? 

Change management refers to the systems and processes in place for helping organizations change the different dimensions of their day-to-day business operations. 

Some changes are granular and incremental, while others are more transformational. Some common examples of organizational change include: 

Updating the company website, 

Creating a new department, 

Creating new positions, 

Launching new marketing campaigns, 

Switching suppliers,

Mission statement changes, 

Digital transformation, and more. 

What is change management automation?

Change management automation is software that leverages automation tools, such as RPA, and a host of other cognitive automation technologies – such as NLP, OCR, and other AI/ML models – to automate the manual steps that are within the change management process.

What are the top 6 use cases of RPA in the change management process?

The change management process usually revolves around the following main steps. We will explain the benefits of RPA within each step. 

1. Filling out a change request

Project name, 

Description of change, 

Reason for change, 

Priority level, 

And various performance KPIs. 

Figure 1: A typical change request form. Source: Project Manager

Getting these forms and manually filling them out is time-consuming. Companies can leverage RPA-enabled chatbots to transform the document-filling process into a two-way conversation. 

So instead of an employee filling each row of information in person, the chatbot will ask questions regarding the details of the change request form to the employee and fill it accordingly.  

The benefit of using chatbots is that the process loses its bureaucratic persona and adopts a more engaging and conversational tone. 

2. Collection and referral of change request

The requests should be collected periodically in order to be assessed, analyzed, and perhaps implemented, as soon as possible. 

For paper-based documents, the person in charge of collecting these forms might forget to do so, or forget to refer them to the person responsible. On the other hand, the employee submitting the request might fill out the form but forget to submit it. 

These mishaps invalidate the whole process of change requests. 

RPA can be used to:

First, transform the transcript between the employee and the chatbot into a machine-readable format. 

This structures the change request and makes sure the data format is aligned with the company’s archiving policy. 

In addition, RPA, via OCR, can extract the change request’s recipient email address. It then can automatically draft an email, attach the PDF version of the change request, create a support ticket on the ERP for the request, and forward it along to the email address.  

The benefit of automating the collection of change requests is that the RPA will immediately send it to the person of authority’s email address, thus eliminating the possibility of delayed collections and pile-up of change requests all in one queue.

3. Preliminary assessment 

Same as with resume screening, where the RPA bot can go through candidates’ resumes for preliminary checks to see if they meet the initial criteria, they can also be leveraged to see which requests make it past the first round of revisions, without human oversight.

The RPA bot will cross-check each row of entries with a series of pre-approved, rules-based metrics to ensure the request is compliant. For instance, the company policy might deny employees with less than 2 months of working with the company to submit change requests. Via API, the software will extract the employee’s information from the database and the RPA will check if the employee making the request passes the 2-months threshold. 

Or the company might want to disregard all automation initiatives that might push back the deadlines of projects that currently rely on the old method of doing things. The RPA will use NLP capabilities to read if the answer to “will it impact the project’s deadline?” is “yes.” It will then reject it, or if the employee’s answer is unclear, then will it be forwarded to a human for further assessment. 

4. Evaluation

Once the request has passed the initial assessment, it can now be fully evaluated by the RPA bot. It could also be partially evaluated, flagged, and sent to the CAB for human intervention.

RPA bot can undertake the evaluation process by leveraging data in a decision table (see Figure 2).

For instance, a salesperson might want to submit a change request for offering one of her/his clients a “Gold discount.” The RPA bot will cross-check the client’s qualifications against predetermined criteria, such as: 

If the client in question has ordered more than 50 units in the last three months, 

And if they have settled their account on delivery. 

If the conditions are met on an “if-then” basis, the bot will automatically approve the request and move it onto the second round of assessment. If not, it will reject and disregard it. 

Figure 2: A sample decision table. Source: Service Now

The benefit of allowing RPA bot to evaluate requests is that, like a foot soldier, they will always carry out their responsibility with respect to the rules-based orders they are given. Moreover, the number of lengthy CAB meetings will also be reduced. Another benefit of automating evaluation is it eliminates possible human prejudices when assessing change requests. 

Case study: 

By leveraging a change management automation solution, they were able to increase their adoption of self-service and self-help features, which allowed employees to autonomously submit their requests and wait for a response, as opposed to getting a human involved. Other benefits included: 

The IT team’s staff dedicated their time to more value-driven tasks, 

The software eliminates ticket backlogs by receiving and assessing requests in a timely manner

5. Communication

A survey has shown that 1 out of every 3 projects in the workplace fails because of miscommunication. That is why it’s paramount for CAB to clearly, and punctually, relay the pending changes to the management, stakeholders, and employees so the workflows can be adjusted preemptively. 

RPA can be used to create automated emails to send to registered email addresses of the stakeholders, and all department employees to which the change is going to affect. The timely sending of these emails prior to a full-blown implementation can allow all relevant personnel to assess how they should approach these changes, or whether they want to be a part of it at all. 

The email thread can also contain a link to Google Calendar, or internal calendar apps, which the company uses, to allow personnel to automatically schedule virtual meetings with each other, the management, and the CAB to go over the details of changes in person (see Figure 3). Moreover, the ML model can also recommend discussion topics for meetings by leveraging the data that is on the change request form and the submitted feasibility study of this transformation, such as high-risk changes, or changes that are yet to win majority support. 

Figure 3: A virtual meeting conducted between colleagues

6. Monitoring

After a certain process has been approved and implemented, process KPIs should be monitored to measure the impact of the actual change. For instance, if the accounting team automated their procure-to-pay (P2P) process, a KPI to monitor is the number of time employees has now saved by letting bots do their jobs for them.

Manually gathering the data from different ERP and DevOps systems can be error-prone and time-consuming. RPA can be used in this stage to gather the reporting data from various ERP applications for data-driven insight. 

Such insights that might go unnoticed if requests are all paper-based and swiftly get archived without much attention can allow the company to increase its agility in terms of addressing employees’ requests.

For more on RPA

To learn more about the use cases of RPA automating other business workflows, read:

If you believe your business would benefit from adopting an RPA solution, we have a data-driven list of RPA vendors prepared.

We are here to answer your questions when it comes to choosing a vendor:

Sources

Change request evaluation case study.

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.

YOUR EMAIL ADDRESS WILL NOT BE PUBLISHED. REQUIRED FIELDS ARE MARKED

*

0 Comments

Comment

Top 7 Use Cases Of Process Mining In Finance

Process mining can be applied in multiple business areas in the financial services industry, such as Purchase-To-Pay, audit, and accounts payable to increase business efficiency and gain a comprehensive overview of business processes to implement and monitor digital transformation strategies. 

This article provides a list of process mining applications and recommendations for financial institutions and businesses that want to leverage process mining. 

Some general and specific use cases of process mining include:

1. Increase process efficiency by discovering bottlenecks

Process mining can be applied to different financial processes that generate event logs such as account payables (AP), receivables (AR), and procurement to visualize process execution and identify bottlenecks. 

Process Mining can provide strategies and recommendations to eliminate process inefficiencies. For example, in claims settlement, mining can identify root causes by measuring the average amount of time to settle a claim.

2. Discover automation opportunities

Leveraging process mining provides insights about processes that can benefit from automation. For example, process mining allows financial organizations to discover the possibility of automation in transactions such as:

purchase-to-pay processes that take longer due to mistakes and manual interventions can be enhanced by implementing automation solutions such as RPA. 

Invoice processes where automation can enable quicker and less costly invoicing by automating repetitive tasks such as data extraction from PDFs.

You can read our in-depth article to learn more on how to implement RPA in finance.

3. Compliance checking

Process mining enables financial institutions to monitor and log process improvement overtime to ensure that their processes are audit-ready. Process mining also provides conformance check and root cause analysis, allowing financial institutions to compare their processes against rules and regulations and analyze the reasons behind the deviations.  

To learn more on specific applications of process mining in compliance and audit:

4. Maverick buying in purchase-to-pay

Banks can leverage process mining in purchase-to-pay, which refers to a company’s entire purchasing process. For example, Process mining can help reduce maverick buying (i.e. purchases of employees without informing the procurement department). Process mining enables the user to check the necessary steps in a P2P process, including:

Generating a receipt after a purchase order (PO)

Matching the PO to a contract: There should not be a PO without a contract (especially if the amount of orders is large in quantity and regular)

5. Root causes for delays and incorrect invoices

Process mining enables users to discover the root causes of delays within the departments. Banks can uncover the reasons for the late fees (e.g. credit card debt payment, loan payments) and minimize late costs, for example, by introducing incentives to customers who pay their debts on time. Also, process mining allows banks to identify the root causes for mistakes and duplicate payments that generate extra workload. 

6. Customer satisfaction

Some process mining techniques (e.g. process discovery) can constantly monitor processes in real-time. Such monitoring enables companies to optimize their processes which increase customer satisfaction. 

For example, process mining can help financial service providers to understand customer behavior by discovering customer engagement insights (e.g. length of call, pattern in issues). Banks that understand customer perspectives can change their operations to improve customer experiences. Process mining allowed many retail banks to discover customers’ difficulties during the bank account confirmation process and provide fast confirmation for opening a bank account. 

7. Risk mitigation 

Process mining helps financial service providers to avoid potential risks when they innovate or modify processes in their systems by constantly monitoring processes and providing data-driven insights and optimization opportunities. 

Recommendations

The best practices to apply process mining in finance include:

Understand the purpose and possible outcomes: Before applying process mining, providers must sort out the main purposes of the process mining application. For example, banks can update their website and FAQ section to prevent unnecessary customer calls. 

Ensure data quality and availability: To implement process mining, banks and financial institutions must identify, collect, and clean the data. 

Assure data security: Financial data is sensitive because it includes privileged customer information (e.g. spending records, credit score). To better protect customer data, financial organizations must leverage privacy-enhancing technologies, such as homomorphic encryption and zero-knowledge-proof to enable process mining users to process confidential data without exposing it to unauthorized individuals. 

Further reading

To discover process mining use cases in banking and insurance, and their benefits, feel free to read:

If you believe you have more questions on process mining, download and read our comprehensive whitepaper:

If you believe your business can benefit from process mining tools, you can check our data-driven list of process mining software and other automation solutions.

And you can let us find you the right vendor:

Hazal Şimşek

Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

YOUR EMAIL ADDRESS WILL NOT BE PUBLISHED. REQUIRED FIELDS ARE MARKED

*

0 Comments

Comment

Top 8 Computer Vision Techniques Entwined With Deep Learning

Edge AI will be a crucial technology for bringing deep learning from the cloud to the edge. Here are 8 computer vision techniques entwined with deep learning. 1. Tumor Detection

In the medical area, computer vision and deep learning applications have shown to be quite useful, particularly in the precise diagnosis of brain cancers. If left untreated, brain tumors spread swiftly to other areas of the brain and spinal cord, making early discovery critical to the patient’s survival. Medical experts may employ computer vision software to speed up and simplify the detection procedure.  

2. Medical Imaging

Computer vision has been utilized in a variety of healthcare applications to help doctors make better treatment decisions for their patients. Medical imaging, also known as medical image analysis, is a technique for seeing specific organs and tissues in order to provide a more precise diagnosis. With medical image analysis, physicians and surgeons may get a better look into the patient’s interior organs and spot any problems or anomalies. Medical imaging includes X-ray radiography, ultrasound, MRI, endoscopy, and other procedures.  

3. Cancer Detection

Deep-learning computer vision models have attained physician-level accuracy when it comes to diagnosing moles and melanomas. Skin cancer, for example, can be difficult to diagnose early since the symptoms are often similar to those of other skin conditions. As a solution, scientists have used computer vision technologies to successfully distinguish between malignant and non-cancerous skin lesions. There are various benefits to employing computer vision and deep learning systems to identify breast cancer, according to research. It can assist automate the detection process and limiting the likelihood of human mistakes by using a large library of photos including both healthy and malignant tissue.  

4. Medical Training

Not just for medical diagnosis, but also for medical skill development, computer vision is frequently employed. Currently, surgeons are not only reliant on the conventional method of learning skills via hands-on experience in the operating room. Simulation-based surgical platforms, on the other hand, have shown to be an excellent tool for teaching and testing surgical abilities. Surgical simulation gives trainees the opportunity to practice their surgical abilities before entering the operating room. It allows them to receive thorough feedback and evaluations of their performance, helping them to develop a better understanding of patient care and safety before performing surgery on them.  

5. Combating Covid-19

The Covid-19 epidemic has presented a major threat to the worldwide healthcare system. With governments all around the world attempting to battle the disease, computer vision can make a huge contribution to overcoming this obstacle. Computer vision applications can help in the diagnosis, treatment, control, and prevention of Covid-19 thanks to rapid technological improvements. In conjunction with computer vision programs like COVID-Net, digital chest x-ray radiography pictures may readily diagnose illness in patients. The prototype program, built by Darwin AI in Canada, has shown results in covid detection with a 92.4 percent accuracy.  

6. Health Monitoring

Medical practitioners are increasingly using computer vision and AI technologies to track their patients’ health and fitness. Doctors and surgeons can make better judgments in less time using these assessments, especially in emergency situations. Computer vision models can assess whether a patient has reached its final stage by measuring the volume of blood lost during surgery. One such application is Gauss Surgical’s Triton, which successfully monitors and calculates the volume of blood lost during surgery. It aids surgeons in determining how much blood the patient will require during or after surgery.  

7. Machine-assisted Diagnosis

These technologies can assist doctors in detecting malignancy by detecting tiny changes in tumors. Such instruments can assist in the discovery, prevention, and treatment of a variety of illnesses by scanning medical images.  

8. Timely Detection of Disease

The patient’s life and death are dependent on prompt identification and treatment for a variety of disorders such as cancer and tumors. Early detection of symptoms increases the patient’s chances of survival. Computer vision applications are educated with large volumes of data, such as hundreds of photos, in order to detect even the tiniest differences with high accuracy. As a consequence, medical practitioners may spot minor alterations that would otherwise go unnoticed by the naked eye.  

More Trending Stories 

Implementing Computer Vision – Face Detection

This article was published as a part of the Data Science Blogathon

Overview

Computational Vision is the part of Artificial Intelligence, which aims to design intelligent algorithms that have the ability to see as if it were a human vision.

In this article, we’ll cover three of the main scopes.

Face Detection

Object Detection

Facial Recognition

Object Tracking

In this first article, we will focus on the introduction of computer vision, and the face identification application based on the Python OpenCV library. In future articles, we will demonstrate the applications of object identification, face recognition, and object tracking in real-time video.

1. Introduction

2. Face Detection Algorithm

3. Face Detection Implementation

4  Alternative to OpenCV

5  Conclusions

6 References

Introduction

The reader of this article will be able to understand the functioning of several visual computing applications, their operation in the background and architecture, and what are the necessary steps to implement an application for real use.

Let’s now look at some of the other applications that can be developed in this area that we’ve already discussed earlier.

Face detection will put a little square when it finds faces and face recognition will put a name for those people. We’re going to do an implementation somewhat similar to this one. We have this image of Kinect from Microsoft which is integrated with the Xbox video game which is motion detection.

You can use computer vision to detect the person controlling the car while moving the steering wheel. Computer vision techniques for image recognition are needed, that is, the robot needs to see what is in front of it to make a decision.

Another example is autonomous cars. You can notice that there is a series of sensors in this car, for example, it needs to detect pedestrians to avoid hitting a person.

You need to detect traffic signs or if you have a traffic light.

If the signal is red it has to stop if the signal is green it has to continue. For this, computer vision techniques are used, including those used. This face detection technology is also used for object detection.

Finally, we have this other example which is called deep Durin which is an image generated by a neural network. These are hallucinogenic images you can see that it has some characteristics some animal traces in parts of this image ie algorithms already have information about the animals about these images of animals are very similar to that idea of ​​evolutionary neural networks and there is an algorithm that will combine the characteristics of an image with that image of the landscape, for example.

An example of application is the deep faces, which are faces of people created by artificial intelligence.

Face Detection Algorithm

The Cascade Classifier is an algorithm you will learn to classify a certain object to start training. We need two sets of images the first set with faces with the positive images you want to detect and the second set of images are called the images negative that are images of anything but ease.

If you for example want to detect cars you will have as positive images the cars various models and types of vehicles as negative images.

Any other type of image and you need to have these two sets of images for you to submit to the algorithm to train.

There is training with this algorithm called Ada boost in the machine learning area. I won’t go into details of how this algorithm works, but basically, you apply this algorithm to both positive and negative images and the idea is to make the selection of features.

We have several features or these little squares with these black and white colors and the idea for you to classify a face and apply those features.

These little squares are for each subwindow of an image.

This window concept indicates that it moves from left to right and top to bottom.

Face Detection Implementation

We will use the Python OpenCV Library, which is one of the main tools on the market for developing Visual Computing applications.

Home

 

Let’s now show our code in Python:

 

import cv2 # OpenCV Import img = cv2.imread('/content/imagem-computer-vision.jpg', cv2.IMREAD_UNCHANGED) # Import Image with Peoples cv2_imshow(img)

detector_face = cv2.CascadeClassifier('/content/haarcascade_frontalface_default.xml') imagem_cinza = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cv2_imshow(imagem_cinza)

deteccoes = detector_face.detectMultiScale(imagem_cinza, scaleFactor=1.3, minSize=(30,30)) deteccoes array([[1635, 156, 147, 147], [ 284, 262, 114, 114], [1149, 260, 129, 129], [ 928, 491, 171, 171], [ 222, 507, 151, 151]], dtype=int32) for (x, y, l, a) in deteccoes: #print(x, y, l, a) cv2.rectangle(img, (x, y), (x + l, y + a), (0,255,0), 2) cv2_imshow(img)

We visualized the processing for the identification of faces through the Google Colab notebook:

Arrests he will return the number 5 indicates that he detected seven faces and we have these points that indicate each of the faces for you to understand better and let’s use this last face.

len(deteccoes) # Fotal Faces= 5 5

Alternative to OpenCV

 

In selecting the alternatives to OpenCV, we adopted the following criteria:

Ease of adoption of the technology

Usability

Scalability,

Robustness

Flexibility

 

Below is a list of my alternatives, following the criteria above:

1) Microsoft Computer Vision API

2) AWS Rekognition

3) Google Cloud Vision API

4) Scikit-Image

5) SimpleCV

6) Azure Face API

7) DeepDream

8) IBM Watson Visual Recognition

9) Clarifi

10) DeepPy

Conclusions

In this article, we use the Python OpenCV library, as a tool that speeds up the identification of Face, in an agile and efficient way.

With the help of this article, the Data Scientist will be able to implement other Visual Computing applications, such as identification of the use of masks, body temperature, social distance in supermarkets, object identification, facial recognition, real-time object tracking.

Reference

Author Reference:

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.

Related

Top 5 Smartwatches To Buy In Early 2023

A smartwatch, also known as a connected watch, offers a lot of features beyond just telling the time. It can serve as a fashion accessory, a fitness tracker, and a notification device. The convenience of having all the essential information at your fingertips. Without having to take out your smartphone makes it a popular choice for many consumers. However, it is essential to be selective about the notifications you receive on your wrist to avoid distractions.

In addition to being fitness trackers, smartwatches also have health features that serve individuals looking for a better quality of sleep. By tracking sleeping patterns, the watch can provide insights on how to improve sleep quality. For those who do not exercise regularly, connected bracelets are also available as an option.

With the abundance of smartwatches in the market, choosing the best watch can be a hard task. We have compiled a list of the best 5 smart watches in 2023 that will be compatible with both Android and iOS devices.

The best smartwatches to buy in early 2023

Samsung Galaxy Watch 5 Pro

One of the standout features of the watch is the 1.4-inch AMOLED screen, which allows for easy interface navigation, even in direct sunlight. The light sensor automatically adjusts the screen’s brightness to match the lighting conditions. There are several watch faces to choose from, and the WearOS operating system is responsible for managing the watch’s interface with the One UI Watch interface. The ecosystem of applications and measurements offered by Samsung is also extensive. Providing users with a broad range of features and functionality.

Despite these drawbacks, the Samsung connected watch’s overall performance remains impressive, with an autonomy of over two days being one of its key selling points. This is a rare feature among smartwatches, and it sets the Samsung watch apart from many of its competitors.

For those seeking a more affordable option, Samsung has also released the Galaxy Watch 5, which is similar to the previous generation of watches. However, the battery life on this model is still limited, and tests suggest that users should consider investing in the Pro version instead. Overall, the Samsung connected watch series provides an excellent daily-use smartwatch with a strong emphasis on sports performance.

Apple Watch Series 7

Are you an iPhone user and searching for a smartwatch? Look no further than the Apple Watch, as it seamlessly integrates into Apple’s ecosystem to create a seamless relationship between your iPhone, smartwatch, and Mac.

Recently, Apple unveiled its latest release in the Apple Watch lineup, the Watch Series 8, which includes new features such as a temperature sensor. It’s worth noting that two other versions are available as well, the Apple Watch SE 2 and the Apple Watch Ultra.

The seventh generation of the Apple Watch may seem paradoxical at first glance, as it primarily focuses on increasing the size of the screen. However, this development has greater implications than initially apparent. While the dimensions of the case itself remain relatively unchanged, with a millimeter added in both height and width, the display takes up significantly more space with a 20% increase in display area compared to the Series 6. This translates to a watch that is easy to read on a daily basis, especially when paired with a 70% brighter Always On mode.

Despite these changes, internal components remain relatively the same, with the new processor primarily managing the additional pixels without burdening the battery life. The battery has also slightly increased in size, mainly to compensate for the larger screen. In practice, the battery lasts for a full day, but charging time is faster when using a 20 W charger. Allowing for 8 hours of autonomy in just 8 minutes of charging.

On the software front, watchOS 8 brings a host of new features. While the Apple Watch Series 7 may not be revolutionary, it does take significant strides forward in terms of its format and charging capabilities, and it remains one of the best smartwatches.

However, the Series 7 will soon be replaced by the new generation, namely the Apple Watch Series 8, and therefore will not be available in stores. While the Series 8 smartwatch only includes a few improvements, the real interest lies in the SE and Ultra versions.

Consider the Apple Watch SE (2024) for a more affordable option. This latest version of the Apple Watch was released alongside the iPhone 14 and has received great feedback in reviews. It offers a wide range of features, from installing apps to tracking fitness activities. Making it a versatile smartwatch suitable for everyday use or sports enthusiasts.

Gizchina News of the week

Join GizChina on Telegram

The user experience is comprehensive, although the watch can only be paired with an iPhone, which is a potential drawback. However, the Apple Watch SE (2024) provides excellent value for its quality and features. Making it one of the best options on the market for those on a smaller budget.

Huawei Watch 3

Huawei, the Chinese tech giant known for its smartphones and other gadgets, has recently introduced its latest model of smartwatch, the Huawei Watch 3. While the brand has been known for its relatively affordable models in the past. The Watch 3 marks a move towards the premium end of the market. Not only that, but it also boasts a new operating system – HarmonyOS. Making it the first Huawei watch to use this technology.

From an aesthetic point of view, the Watch 3 impresses with its elegant design and impeccable finish. However, it is also quite big in size, which could be a concern for people with smaller wrists. Nevertheless, the Watch 3 is available in several variants, including elastomer and steel bracelets, which fit different preferences.

The Watch 3’s digital crown on the edge of the device is similar to that of the Apple Watch, offering easy navigation. Meanwhile, the AMOLED display panel is bright and readable in all circumstances, adding to the watch’s convenience. The Huawei Health app offers nearly 300 references for different watch faces, although many of these are not free options.

When it comes to activity monitoring, the Watch 3 provides all the classic measures, except for an electrocardiogram. However, it does have a unique feature: the ability to measure skin temperature. Which can warn wearers in case of fever. The battery life is average, lasting around 3 days on a single charge.

One of the most interesting aspects of the Watch 3 is the HarmonyOS. Which is a relatively new operating system that Huawei has developed to compete with Android and iOS. The watch’s interface is well thought out, with intuitive features and easy navigation. However, the downside is that third-party applications are still very few, despite the system being open to developers.

Fitbit Sense

Fitbit is a well-known brand in the fitness and health tracking market. Offering a range of activity trackers and connected watches. While their watches have been met with mixed success, the Fitbit Sense has recently surprised consumers with its 100% healthy approach. Embedding all the sensors available to date.

The Fitbit Sense offers a range of sensors, including the very classic cardiac sensor, ECG, temperature sensor, electrodermal activity sensor, and oxygen saturation monitoring. With just over four days of battery life, the Sense is a reliable device that provides long-lasting monitoring.

Overall, the Fitbit Sense is a highly reliable watch that provides a comprehensive approach to health monitoring. While it may not offer all the features of a full-fledged smartwatch, it is highly regarded for its focus on health and wellness.

Redmi Watch 2 Lite

Xiaomi, the Chinese electronics company, has recently released a new smartwatch model under the Redmi brand. Which is targeting the affordable smartwatch market. The Redmi Watch 2 Lite adopts a rectangular square format, which is visibly similar to the Apple Watch. However, unlike many of Xiaomi’s other watches, this watch is more basic and comes with a single button on the edge and an all-plastic finish.

Despite its basic features, the Redmi Watch 2 Lite offers a more than decent LCD screen for the price. However, the Always On mode is not available on this watch. The motion detection feature, on the other hand, is relatively effective. Providing users with an improved experience compared to other smartwatches in the same price range.

When it comes to the watch’s application features, users are limited to what is pre-installed on the watch. Additionally, the watch is dependent on the user’s smartphone for connectivity. However, the pre-installed apps are basic yet effective, and cover most everyday uses. Furthermore, the watch offers cardiac monitoring. Which is an essential feature for individuals who are keen on monitoring their heart rate. Additionally, the watch can be in use to track physical activity with relative ease.

The Redmi Watch 2 Lite has an autonomy of about two days, which is considered adequate for normal use. The charging of the watch is done through a proprietary cable, which may not be as convenient for some users. While the watch is not expected to revolutionize the smartwatch market, it provides a good compromise between a connected bracelet and a more expensive smartwatch.

Verdict

In conclusion, the market for smartwatches in 2023 is full of vast options that cater to a range of needs and preferences. Smartwatches offer convenience, style, and functionality all in one device. They are an excellent tool for fitness enthusiasts, athletes, and those looking to monitor their health and fitness progress. With the best smartwatches in 2023, you can expect a range of features. Including heart rate monitoring, GPS, SpO2 monitoring, and long battery life. Whether you are looking for a stylish accessory or a tool to help you achieve your fitness goals, the above-listed smartwatches are the best.

Update the detailed information about Top 5 Computer Vision Use Cases In Agriculture In 2023 on the Eastwest.edu.vn website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!