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is one of the leading occupations among the people in rural areas lacks due to underdeveloped methodologies or use of outdated know-how.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

What is the Difference Between AI, Machine Learning & Deep Learning?

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ai vs machine learning vs deep learning

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the most widely used interchangeable words creating confusion among many people globally.

Although, these three terminologies are typically used interchangeably, but they all are different from each other especially in terms of their applications, capabilities, and results.                 

Understanding the difference between AI, ML, and deep learning is important to utilize the precise applications of these jargons and take the right decision while dealing with AI, ML, or DL related projects.

Before we start, I would like to show you few images (see below) that will give an overview, how AI, ML, and DL are different from each other or how these three terminologies are related to each other.

The easiest way to understand their relationship is to visualize them as concentric circles with AI — which is a broader area, then ML — which is the branch or subset of AI, and finally deep learning — which is a part of the subset of ML, fitting inside both or you can say – DL is driving today’s AI explosion due to more complex inputs and outputs.

I think these highly illustrative images cleared some doubts and misconceptions about these jargons. But you need to go through more definitions with a few sets of useful examples and use cases that will help you understand these concepts better.

What is Artificial Intelligence?

As the name denotes, AI is a broader concept used to create an intelligent system that can act like human intelligence.  The terms – “Artificial” and “intelligence” means “a human-made thinking power”.

Basically, AI is the field of computer science used to incorporate human intelligence into machines, so that such machines or systems can think (not exactly) and take sensible decisions like humans. 

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

And such AI-enabled machines can perform specific tasks very well and sometimes even better than humans — though they are limited in scope. And to develop such machines AI training data sets are processed through machine learning algorithms. 

To be more precise, AI-enabled systems don’t need to be pre-programmed, instead such algorithms are used, that can work with their own intelligence. And machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used to create such systems.

Example of AI in Daily Life

Smart Home Devices, automated mail filters in our Gmail, Self-driving cars, Chatbots, AI Robots, Drones and AI Security Cameras are the popular examples where AI in integrated. Though, there are many more other applications, devices, systems and machines works on AI principles helping humans in various areas across the globe.

Also Read: How Can Artificial Intelligence Benefit Humans

What is Machine Learning?

As the name suggests, machine learning empowers the computer system to learn from past experiences earned through training data. As of now, you got to know machine learning is the subset of artificial intelligence, in fact, it is the technique used to develop AI-enabled models.

What is Machine Learning

Machine Learning is used to create various types of AI models that learn by themselves. And as much as it gets more data, it gets better at learning and gives more accurate results.

Let’s take an example of how machine learning and algorithms work while making predictions. ML is actually a process of training the algorithms to learn and make the decisions as per the learning.

While training an ML-based model, we need certain machine learning training data sets to feed into the algorithm allowing it to learn more about the processed information.        

Today, machine learning is being utilized in a wide range of sectors including healthcare, agriculture, retail, automotive, finance and so many more.

Machine Learning Examples in Real Life

Recommendation on your Mobile or Desktop based on your web search history, Virtual Assistance, Face & Speech Recognition, Tag or Face Suggestion on Social Media Platforms, Fraud Detection, Spam Email Filtering, are the major examples of machine learning in our daily life. Most of the AI devices are developed through machine learning training.    

What is Deep Learning?

It is the subset of machine learning that allows computers to solve more complex problems to get more accurate results by far out of any type of machine learning. 

Deep learning uses the Neural Network to learn, understand, interpret and solve crucial problems with a higher level of accuracy.

What is Deep Learning

DL algorithm-based neural networks are roughly inspired by the information processing patterns that are mainly found in the human brain. 

While learning, understanding, and predicting just like we use our brains to recognize and understand certain patterns to classify various types of information, deep learning algorithms are mainly used to train machines for performing such crucial tasks easily.

Whenever we try to perceive new information, the brain tries to compare it with the items known to the brain before making sense of it. In deep learning – neural network algorithms employ to perceive new information and give results accordingly.

Actually, the brain usually tries to decode the information it receives and archives this through classification and assigning the items into various categories.

Let’s take an example – As we know DL uses a neural network which is a type of algorithms aiming to emulate the way human brains make decisions.

The notable difference between machine learning and deep learning is that the later can help you to understand the subtle differences. Because DL can automatically determine the features to be used for classification, while ML needs to make understandable these features manually. 

Finally, the point is compared to ML, DL requires high-end machines and a substantially huge amount of deep learning training data to give more accurate results.

Deep Learning Examples in Real Life

Automated Translation, Customers Shopping Experience, Language Recognition, Autonomous Vehicles, Sentiment Analysis, Automatic Image Caption Generation & Medical Imaging Analysis are the leading examples of deep learning in our daily life.           

Summing-up 

Machine learning is already being used in various areas, sectors, and systems but deep learning is more indispensable for the healthcare sector where the accuracy of results can save the lives of humans. Though, countless opportunities lie for machine learning and deep learning to make the machines more intelligent and contribute to developing a feasible AI model.

In the healthcare and medical field, AI can diagnosis disease using the medical imaging data that are fed into deep learning algorithms to learn the tumors or other life-threatening diseases. Now deep learning is giving excellent results, even performing better than radiologists

Finally, in all types of AI, ML or DL models working on computer vision-based technology needs a huge amount of training data for object detection. These datasets help them to learn the patterns and utilize similar information for predicting the results when used in real-life.

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Whatsapp

How to Enable Two Factor Authentication on Your WhatsApp Account?

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how to enable two factor authentication whatsapp

Do you know if someone manage to get your mobile number, he/she can install and run WhatsApp on his smartphone? Yes, it is possible if you have not enabled two factor authentication on WhatsApp to prevent such unauthorized use.

WhatsApp OTP Scam is the latest technique of fraud in which fraudulent people can misuse your phone to tease people or ask for money transfer on your behalf or commit some kind of other crime or scam that will get you in major trouble.

Must Read: WhatsApp OTP Scam: Know About This Online Fraud & How to Stay Safe

But if you enable two factor authentication on your Whatsapp account you can prevent such frauds. The 2-factor authentication means every time when you install WhatsApp on your registered mobile number you will be asked for authentication.

The two-step verification PIN is different from the 6-digit registration code that you receive via SMS or phone call to verify your phone number with a registration code to activate your account.

What is Two Factor Authentication on Whatsapp?

Two-step verification is an optional feature that adds more security to your WhatsApp account and authentication is required while installing Whatsapp. And can see the two-step verification screen after you successfully register your phone number on WhatsApp.

When you enable two-step verification, you have the option to enter your email address. This allows WhatsApp to email you a reset link in case you ever forget your PIN, and also helps safeguard your account from unauthorized use.

How to Enable Two Factor Authentication Whatsapp?

To enable the two factor authentication on whatsapp first you have to get ready your existing Whatsapp installed and activated into your phone with registered mobile number. If you have already activated just follow the steps given below.

Steps to Enable Whatsapp 2-factor Authentication:

Step1: Open WhatsApp on Your Phone.

Step2: Now click on Three Dots at the top-right corner.

Step3: Click here on Settings and Tap on Account.

Step4: Now tap on Two-step verification.

Step5: Here you will see screen to Enable the 2-step verification.

Step6: Now enter a Six-Digit PIN of your choice and confirm it.

Step7: Here, at this screen you will be asked to enter your email address that you can Skip, but if you add an email address, it will help you to reset two-step verification in case you forgot your 6-digit PIN.

Step8: Now confirm the email address and tap Save or Done.

Note: If you don’t add an email address and you forget your PIN, you’ll have to wait 7 days before you can reset your PIN. Since, WhatsApp not verify this email address to confirm its accuracy, make sure you provide an accurate email address you can access.

Once you have enabled the two-step verification, every time when you install and run Whatsapp on your registered mobile number on new phone you will be asked to enter this 6-digit verification code.

However, while using Whatsapp on your phone to help you remember your PIN, WhatsApp will prompt you to periodically enter your PIN.  

You can change PIN or disable two step verification code in whatsapp on your account anytime. But online scam, especially account connected with your mobile number is on the rise. Fraudulent people in the ambush to hack your mobile your or get access of such applications and commit crime or illegally transfer money from your account.

Thus, enable this 2-factor authentication now and keep your whatsapp account safe from unauthorized access. If you not able to understand how to enable this 2-step verification code, you can watch the video below uploaded by WhatsApp.

Watch Video to Enable Whatsapp Two Factor Authentication:

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Mobile Apps

WhatsApp OTP Scam: Know About This Online Fraud & How to Stay Safe

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WhatsApp OTP Scam How to Stay Safe

WhatsApp has more than 1 billion users worldwide, preferably used for chatting and sending text or multimedia messages. Being one the most widely used chatting mobile apps, it is also attacked for committing fraud and scams.       

Yes, I’m talking about the WhatsApp OTP Scam, which is trending nowadays. Few fraudulent people are sending fake messages attempting to lure users into phishing traps. Just like other this WhatsApp scam asking for money from your friends.  

This new type of whatsapp scam asking for code hacks your friend’s account and sends you personalised messages or ask to transfer money. Hence, here we brought everything you need to know about this scam and how to stay safe.      

What is WhatsApp OTP Scam?

In this scam, the fraudulent user sends you a message requesting to be your friend and will describe some kind of emergency to get your attention. Sometimes, these messages may come from your friend’s WhatsApp number itself. 

After telling the emergency and pretending to be a friend in need, the scammer will send you a WhatsApp message through which he will ask about an OTP.

WhatsApp Scam Asking for Code

After pretending to be a friend in need, scammer may pretend to have sent you an OTP mistakenly on your smartphone and then request you to forward it to him. In reality, the scammer simply wants to access your WhatsApp account.

scam whatsapp code

Actually, the OTP that gets generated is basically to verify your WhatsApp account. The scammers send fake messages so that you end up sharing that OTP.

WhatsApp Scam Asking for Code

As the OTP is meant to verify your WhatsApp account, the moment you share it with someone else, that person will get full access to your WhatsApp account and WhatsApp on your mobile will be locked out. 

Also Read: How To Stop Someone from Adding You To A Group In Whatsapp

After the WhatsApp account gets hacked, the fraudster sends messages to the friends of the contact to continue with the scam.

WhatsApp Scam Asking for Money

Finally after getting the unauthorized access of your WhatsApp account, apart from sending offensive messages, fraudsters ask to send money.

Technology brings not only innovations but also attracts fraudulent people to take advantage of technical jargons that common people don’t understand and became the victim of such frauds.      

Also Read: How to Stop Whatsapp Media Auto Download to Gallery

Yes, using your mobile he might ask to transfer money from your friends pretending that he is having some kind of emergency and don’t have sufficient bank balance to meet his requirements and later on will return your money.   

Nowadays, not just WhatsApp, cybercriminals posing as Income Tax officials or Bank, Loan, Credit Card or other finance executives, use emails and phone calls to trick people to reveal their financial details. So, beware of these activities.

How to Prevent WhatsApp OTP Scam?

To prevent this WhatsApp OTP Scam the best option is activate two-factor authentication on your WhatsApp account. Yes, this 2-step verification prevents your WhatsApp account number to be hacked without your authorization.

And if you don’t know how to activate or enable this two-factor authentication on WhtsApp, you can read another blog linked below. And also remember never share any kind OTPs with anyone, as OTPs are not generated to share with others.

Also Read: How to Enable Two Factor Authentication on Your WhatsApp Account

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