It is precise to say that mobile phones have now become an integral part of our day to day life. With regards to Android and iOS enhancements, organizations often get confused with what should they opt or offer their clients. They are in a dilemma to move forward with a multipurpose application with unique User Interface (UI) and amazing User Experience (UX) or applications that are ideal with plentiful stages and are swifter to build in any scenario. With this situation, there comes the REACT Native Development in the depiction.
The React Native Framework is known as the Future of Hybrid Apps. Let’s see, What are the reasons behind it?
Formerly, Facebook just created React Native to be of assistance to iOS platform. Besides, with its partial help to the Android development frameworks and Android working platform. The library would now be able to render versatile UIs for the two stages.
So, Facebook decided to utilize React Native to manufacture its own Ads Manager application, making both an iOS and an Android version. A similar group of engineers worked on the two variants. Facebook additionally made React Native open-source, with the possibility that similarity with different stages like Windows or OS could be chipped away at by the improvement network, so it can remain tuned.
Gone are the WebView components of other cross breed portable apps. This is feasible in light of the reality that React Native’s building squares are reusable “local segments” that get together purposely to local. Components you would utilize in iOS or Android have partners spot on in React, so you will get a consistent look and feel.
This part based structure furthermore empowers you to fabricate apps with a more quick, web-style way to deal with progression than compared to common half breed solutions, yet with no web by any chance. The application will have the swiftness, look, and expediency of a local portable system.
This is a massive reward for organizations that require extending an in progress application, however, would have a preference not to update it. Fuse React Native parts into the app’s code. Or on the other hand, if your existing half breed application was built with Cordova and Ionic, you can reuse that Cordova-based code efficiently with a module.
React is growing speedily and Facebook has initiated its advanced plans to carry on investing in its overall expansion.
While native app development is more often than not connected with inadequacy, lesser developer productivity, and slow time of deployment, React Native is all about enhancing the overall pace and agility of web application development to the overall hybrid space with native outcomes.
Beneath React Native’s hood is Facebook’s trendy ReactJS UI library for different web applications. React Native enables all of ReactJS’s superior app performance, DOM abstraction, and easy programming methodologies to hybrid mobile app development.
The 3rd party plugins mean you won’t have to depend on a WebView for specific functions. For example, if you are adding Google Maps features and functionality to your application, React Native lets you connect the plugin with a native module, so you can connect the map up with the device’s specific functions including rotate, zoom, and the compass while using lesser memory and loading rapidly. If your application backs old operating systems, this can assist you to keep the application run very smoothly.
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Written By : Umesh - Android Team Lead
This Article is updated on 16 September 2020.
Frameworks and libraries are distributed with different licenses. There are some licenses which are somewhat flexible and allows for adjustments according to the requirements. They are known as open-source products and there are other licenses which do not allow for the designing of commercial applications or the flexibility of editing the content after its creation.
Both Reactjs and Angular are open-source products and when it comes to the popularity of React vs Angular, both are used by major brands.
You can see here the Google trends comparison graph of Angular vs react.
Yes! You can see here the result. In Google trend comparison, react is a clear winner.
Before you even move even a step ahead, it is suggested to glance through the synopsis of the Angular Framework and the React Library to decide on your next project.
Check here the comparison of both angular and react.
|Usage||It is used for setting up needed processes and introducing to the quality standards for preventing the errors and flaws in an app.||It is used for identifying and correcting the bugs as well as errors.|
|App Architecture||None||Component based|
|Data Binding||One way data binding||One way data binding, Two way data binding|
|DOM||Virtual DOM||DOM, Regular|
|Last Version||16.13.1||Angular 9.0|
Let’s see more about Reactjs and Angular.
When there is a lot of dynamic content in your application that is when React would be the right choice. Many popular brands like Instagram and Facebook prefer to use ReactJs to base their mobile apps on because of its dynamic nature.
Sometimes, adopting React may make sense over using Angular and vice versa. Here are a few brands which have used Reactjs: Uber, Dropbox, Netflix, Instagram, PayPal, and Flipkart.
Angular was initially released on 14 September 2016 by Google. It is a typescript based web application framework.
Angular follows both one-way data binding and two-way data binding structure.
Angular is frequently updated its features and releases. Now, Angular 7.0 is the latest version. You can check here for the updated Angular version releases.
When you require your app to be compatible with all the browsers that is the best time you use Angular. It even helps your app consistent with browsers older than the IE8. There has also been a recent release of the Angular 5.0 in the market and it has undoubtedly attracted a lot of attention from developers especially with its performance boost features and server-side rending support services. If your mobile application lies between the complexities of low to medium, Angular is the best way to go.
Some of the popular brands using Angular are: NASA, HBO, Nike and YouTube.
Angular 4 is backward compatible right with Angular 2 and further. The different versions that are below Angular 2 are not fully backward compatible. This is one of the disadvantages of Angular as a JS framework. On the other hand, React 16 is entirely backward compatible with preceding versions and even comprises support for asynchronous rendering. It’s also totally open-source and licensed right under MIT alongside GraphQL as well as Jest. Angular 2 is not backward compatible with AngularJS.
Angular and React.js come with strong ecosystems. React is simpler to grasp; however, it needs numerous integrations such as Redux to use the perspective completely.
One more thing or stuff to consider is that as React is a library, you can integrate it into any of the diverse projects, also if the project is developed in Angular.
React has increasing and most incredible popularity amid developers on NPM and GitHub. React has more than 156k stars right on GitHub, which converses its popularity as a framework specially built for the developers’ community. But even Angular has got good community support. A survey highlighted that around 75 % of the React users would utilize it again for different projects.
React has an outsized community base for the reason of Virtual DOM implementation. With Virtual DOM, the apps can be updated and rendered swifter than Angular applications. However, Angular has been supported by and used comprehensively by the apps built by Google, like Google AdWords.
So, both frameworks have significant community support on most of the platforms. Community support assists you in solving any issues that you may face across any particular project.
There is no doubt Facebook and Google have not built React and Angular just back the developers across the globe. They have also developed or supported them to run their own applications and websites on these particular systems. The platforms Instagram, WhatsApp, and Facebook itself are enabled by React, while most of the Google services are managed to utilize Angular.
Even there are many other leading companies other than these IT giants who use these platforms. There are really big numbers. For example, amongst React.JS companies, one can find the New York Times and Netflix, to name a few. Captivatingly, codeacademy.com, which is an accepted website committed to teaching other people to code, is also utilizing React to enable their web applications.
Angular, on the other side, is utilized by leading companies like Microsoft. That should not be that astonishing as TypeScript itself has been built by the creators of Windows. The giant Microsoft utilizes Angular to facilitate its Office’s and Xbox’s sites. Amid other Angular companies, one can find Forbes, Delta Airlines, and Santander Bank.
It is all about the flexibility and capability of a developer to deploy a code the way you want it to run. If you want to achieve a faster loading time, developers much first aim at loading the minimum number and then continue when its demand increases. This gives you the ability to develop features without degrading the loading time.
When it comes to React vs Angular, React allows developers to make the best use for slower loading pieces of code. Its library also enables working with solutions like web pack. Angular offers a limited ability to control the packaging, and as a result, it ends up looking like assembly code. Thereby, Angular is comparatively more rigid. So, while dealing with packaging, React is better.
Angular has a steeper learning curve compared to Reactjs and so, it is not easy to master. If you are a professional developer, this will be easier to understand. Angular has a complicated lifecycle, and you need to deal with its unintuitive link and compile it. React on the other hand has a lower learning curve and takes lesser time for developers to understand its one-way data flow. Its library has very few lifecycle methods and you hardly ever have to read it. So, regarding the learning curve, React is better.
Model complexity means how you would structure your app data model when it is represented by the view. When it comes to this, Angular is considered quite sensitive when you deal with its scope because of its copy and compare feature. This means that it will restrict you from using large app models. However, the outcome will come with an advantage of making the code testable and simple. React gives you the option to choose without impacting the performance of the app. So, regarding model complexity, both React and Angular are good.
You should decide on the Angular framework when building a large-scale application with numerous features as it offers a scalable and trustworthy framework.
On the other hand, if you need to build single-page, light, and cross-platform apps effortlessly, extending an application’s present abilities in a shorter time span, react is the better option.
Angular is bigger and has longer load times on mobile. React is comparatively smaller, so it is faster than Angular.
If you are looking precisely at the present job market and development projects’ point of view, you should explore and learn Angular or React in 2020.
React.js utilizes virtual DOM; on the other hand, Angular 2 works on real DOM. As virtual DOM enables us to update the alterations without rewriting the complete HTML doc virtually, it helps updates to be faster and makes sure swift performance without considering the overall apps’ size.
Angular is a comprehensive full-blown framework, so a fresher requires learning many things such as Typescript that are too in-depth. On the other side, React has less number of concepts to explore and learn when compared to Angular.
Finally, while dealing with the comparison of angular vs. react, both have their own benefits in front-end development, and it is tough to choose one between them.
However, if you are still not sure between them, please get in touch with us at Technostacks, and we can discuss your current or upcoming projects. Our technology specialists are able to assist you in making the best preference based on your business needs and requirements, as well as find the best professionals for your project. Whether you need a development team, project managers, or other technology experts, we can offer you topmost professionals with proficiency in your domains and the project or solutions you need to implement.
Both Angular and React are equally useful for writing applications. But they are entirely different frameworks to use. Some programmers may say that Angular is superior to React and vice versa. What’s in actuality best for an in-hand project is the way you utilize these frameworks.
If you have any question or planning to develop angular or react application then you can hire us. We have experienced team of JS programmers who are able to full fill your requirements. Hire dedicated angularjs developers and node developers from us.
Written By : Technostacks
The global view-point of machine learning frameworks is constantly advancing. Artificial intelligence combined with the correct profound learning system has intensified the general size of what organizations can accomplish and get inside their areas. Also, with an ever-increasing number of organizations hoping to scale up their tasks, it has turned out to be indispensable for any organization to assimilate both machine learning and also prescient examination.
Every system here works alternately for various purposes. Here, we will take a quick look at the best Machine Learning Frameworks to give you a superior thought of which method will be the ideal fit or come helpful in understanding your business challenges. And further, help you in creating machine learning applications with frameworks as well as the most popular machine learning frameworks on which the scientists and the developers are working.
Currently, TensorFlow is the top in the list of Machine Learning frameworks. Most developers are using Tensorflow because it has a great support community and many inbuilt features.
It is outstanding amongst other profound learning structures and has been embraced by a few Goliaths, for example, Airbus, Twitter, IBM, and others for the most part because of its exceedingly adaptable framework engineering.
The most outstanding use instance of TensorFlow must be Google Translate combined with capacities, for example, common dialect handling, content arrangement/rundown, discourse/picture/penmanship acknowledgment, anticipating, and labeling.
TensorFlow is accessible on both work area and versatile and furthermore underpins dialects, for example, Python, C++, and R to make profound learning models alongside wrapper libraries.
TensorFlow accompanies diverse instruments that are broadly utilized. TensorBoard is used for compelling information perception of the system demonstrating and executing TensorFlow serving for the quick arrangement of new calculations/tests. Along with that, it holds a similar server engineering and APIs.
It likewise gives coordination to other TensorFlow models, which is unique about traditional practices and can be reached out to serve different model and information composes.
In case you are stepping towards profound learning, it is an easy decision to decide on TensorFlow given that is Python-based, is supported by Google, and comes stacked with precise documentation and walkthroughs to be managed well.
Caffe is a deep learning system that is strengthened with interfaces like C, C++, Python, and MATLAB and also the order line interface.
It is outstanding for its speed and transposability and its pertinence in displaying convolution neural systems (CNN). The most significant advantage of utilizing Caffe’s C++ library (accompanies a Python interface) is the capacity to get to access systems from the profound net archive Caffe Model Zoo that are pre-prepared and can be utilized promptly. With regards to demonstrating CNN’s or illuminating picture handling issues, this ought to be your go-to library.
Caffe’s greatest USP is speed. It can process more than 60 million pictures every day with a solitary Nvidia K40 GPU. That is 1 ms/picture for deduction and 4 ms/picture for learning — and later library adaptations are even quicker.
Caffe is a prominent profound learning system for visual acknowledgment. Not with standing, Caffe does not reinforce fine-granular system layers like those found in TensorFlow or CNTK. Given the design, the general help for broken systems and dialect displaying its very poor, and building up complex layer composes must be done in a low-level dialect.
The Microsoft Cognitive Toolkit (beforehand known as CNTK) is an open-source profound learning system to prepare scholarly learning models. The tool is prominently known for simple preparing and the blend of mainstream, which demonstrates crosswise over servers. It performs proficient convolution of neural systems and making for the picture, discourse, and content-based information. Like Caffe, it is supported by interfaces, for example, Python, C++, and the order line interface.
Given its smarter utilization of assets, the usage of fortification learning models or generative ill-disposed systems (GANs) should be possible effectively utilizing this toolbox. It is known to give higher execution levels and adaptability when contrasted with toolboxes like Theano or TensorFlow while working on multiple types of machines.
Contrasted with Caffe, with regards to concocting new complex layer composes, clients don’t have to execute them in a low-level dialect because of the fine granularity of the building squares. The Microsoft Cognitive Toolkit underpins both RNN and CNN sorts of neural models and along these lines is equipped for taking care of pictures, penmanship, and discourse acknowledgment issues. As of now, because of the absence of help on ARM engineering, its capacities on versatile parameters are genuinely restricted.
Torch is a logical figuring structure that offers wide help for machine learning calculations. It is a Lua-based profound learning system and is utilized generally among industry goliaths, for example, Facebook, Twitter, and Google. It utilized CUDA alongside C/C++ libraries for handling and was fundamentally made to scale the creation of building models and give in general adaptability.
Starting late, PyTorch has seen an abnormal state of appropriation inside the profound learning structure network and is viewed as a contender to TensorFlow. PyTorch is fundamentally a port to the Torch penetrating learning system utilized for building profound neural systems and executing tensor calculations that are highly advanced along with their multifaceted nature.
Instead of Torch, PyTorch keeps running on Python, which implies that anybody with an essential comprehension of Python can begin without anyone else’s profound learning models.
Given PyTorch structure’s building style, the whole profound demonstrating process is far more natural and additionally straightforward contrasted with Torch.
You can’t ignore MXNet when preparing the list of best machine learning Frameworks. MXNet (articulated as blend net) is a profound learning system upheld by Python, R, C++, and Julia.
The brilliance of MXNet is that it enables the client to code in an assortment of programming dialects. This implies you can prepare your profound learning models with whichever dialect you are agreeable in without discovering some new information sans preparation. With the backend written in C++ and CUDA, MXNet can scale and work with a horde of GPUs, which makes it fundamental to endeavors. A valid example: Amazon utilized MXNet as its reference library for profound learning.
MXNet underpins long here, and now a memory (LTSM) organizes alongside both RNNs and CNN’s. This profound learning structure is known for its capacities in imaging, penmanship or discourse acknowledgment, determining, and NLP.
Exceptionally great, dynamic and intuitive, Chainer is a Python-based profound learning structure for neural systems that are planned by the run procedure. Contrasted with different structures that utilize a similar technique, you can change the systems amid runtime, enabling you to execute discretionary control stream articulations.
Chainer sustains both CUDA calculations alongside multi-GPU. This deep learning system is used principally for assumption investigation, machine interpretation, discourse acknowledgment, and so on utilizing RNNs and CNN’s.
Keras is falling under the category of open source machine learning frameworks Known for being moderate, the Keras neural system library (with a supporting interface of Python) supports both convolution and repetitive systems that are equipped for running on either TensorFlow or Theano. The library is composed in Python and was produced keeping brisk experimentation as its USP.
Because of the way the sensor flow interface is designed it is little bit testing combined with the idea that it is a low-level library that can be many-sided for new clients. Keras was worked out to give a short-sighted interface to the reason for quick prototyping by developing compelling neural systems that can work with TensorFlow.
Lightweight, simple to utilize, and extremely direct with regards to building a profound learning model by stacking various layers: that is Keras more or less. These are the specific reasons why Keras is a piece of TensorFlow’s center API.
The essential use of Keras is in characterization, content age and outline, labeling and interpretation, alongside discourse acknowledgment and the sky is the limit from there. If you happen to be a designer with some involvement in Python and wish to plunge into profound learning, Keras is something you should look at.
It is apparent that the approach of profound learning has started with numerous tools who utilize instances of machine learning and human-made reasoning. Separating assignments in the least difficulty of course and with the end goal of helping machines work more productively has been made feasible by insightful learning.
Some of the Machine Learning Frameworks from the above rundown would best suit your business prerequisites? The response to that lies on various variables or on the off chance that you are looking to merely begin, at this point with a Python-based profound learning system like TensorFlow or Chainer.
In case you are searching for something more, at this point with assets like speed and swift utilization alongside the intelligibility of the prepared model you ought to check out all the parameters before choosing a profound learning system for your business needs.
Written By : Technostacks
Augmented Reality is one of the best things and the most exciting technologies around for the people in this generation. AR was first featured in movies and people thought it is only restricted to entertainment purpose which is not the case today. In fact, Best Augmented Reality Apps for iPhone and Android have made a great impact on our real life. The inception of AR has made learning easy, gaming all the more fun, with the realistic looking scenarios and the 3D view of maps while driving and getting to know the predicted time you will take to reach your targeted destination.
All this is possible through this amazing technology and related tools. There is no second thought that AR applications for Android and iPhone have to do much more in the near future. Let us have a look at the Best Augmented Reality Apps in 2020 that presently exist in the diverse application development marketplaces and how they can be utilized to change some of the needed set-ups.
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This App has transformed the way how learning a different language was pursued in the past. You needed books, dictionaries, and a tutor and spend a good amount of money for a class of a few hours in a week.
MondlyAR eliminates the tedious long-term affair and brings to you the first ever AR language learning App that uses speech recognition and tries to supplement your learning experience with the human interaction. The virtual learning assistant helps you to learn phrases and words hence making the app more interactive by leveraging the speech recognition technology.
The app is fun and exciting as it teaches you how to pronounce the words in its respective language. MondlyAR is available for both Android and iOS users. One can learn Spanish, English, French, German, Italian and 20 more languages which get added to new updates.
Image Source:- beccarama.com
If AR is your recent thing then you would not prefer anything else then using AR to play games. Ingress is one such thing in the gaming world that you should check. This app engrosses virtual world where you need to choose a side – the Resistance or Enlightened. This mission-based app makes you find virtual portals in the real world and you need to capture them.
The app is based on a complex sci-fi world and requires you to move around in the real world in order to capture and protect locations. Though this app lacks accessibility in comparison to other apps available in the market, however, it was able to capture a huge community in just a few months of being launched. The app is available on Android and iOS both.
Image Source:- uploadvr.com
Without mentioning Pokemon Go, the list of the best augmented reality apps would be incomplete. The app immediately became every game hot favorite. The unique concept of going out in the real world, walking around and catching Pokemon – the unique concept brought up by Niantic’s through AR is a must have in every games phone.
The app uses your phone’s GPS location to mark your location whereas the camera is used to locate Pokemon in the real world. The game is easy and does not need to require many instructions to read. This easy to play and understand the game has been the best augmented reality game app available on the Play Store and the App Store.
Image Source:- komando.com
How about an app helping you to style your room or office instead of an interior designer who would charge you a good amount even for consultation? Amikasa is at your rescue and is a great virtual assistant help your style your room before you decide to buy a piece of furniture. Be it your office, garden area, hall, kitchen or bedroom with the help of your iPhone or iPad’s camera you need to simply walk around the space and place 3D models of various furniture pieces to understand how it would look in the real world.
The app allows you to change the color of the furniture based on the color of the room. You can buy the furniture right through the app. It gives the facility of sharing your room design with your family and friends to ask for an opinion. For now, the app is available only on the App Store.
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Google Translate is traveler’s favorite app. It enables one to have a conversation with people even when you do not have a common language. The app is not restricted only to spoken words. The app allows users to even translate road signs, menu and other texts on the go which are in a different language with the use of phone’s camera.
Not only that, the AR feature allows translating your handwriting audio or even a real-time conversation with someone with a different language. Along with the features the app has visual flair and easy to use. The app is available on the App Store and the Play Store.
Image Source:- harveymaps.co.uk
If you are one of those adventurous souls, then you should definitely try out this app. ViewRanger app is designed for hikers who are in the look out of the next great adventurous trail.
The app has an inbuilt library that allows you to search through; the GPS feature identifies your location and suggests the nearest trail. The inbuilt street, aerial, satellite and terrain maps helps you in locating the latitude and longitude. Over nine million locations across the world have been listed in the app and the database is been updated on a regular basis.
Image Source:- microsoft.com
Everyone has a little artist within, however, time doesn’t permit to practice that and turn it into a hobby. There come SketchAR to turn your dream into reality.
SketchAR brings out the creative side of yours through Augmented Reality. All you have to do is simply draw a few circles onto the paper and the app allows you to trace around it. You will have to properly trace those lines, initially you will struggle to match the lines but eventually, you will get hold of the techniques.
In fact, holding a phone in one hand drawing with the other is pretty uncomfortable however it’s worth trying. Mostly ARCore phone is the best for this app. SketchAR is available on Play Store and App Store both.
Image Source:- blog.theroar.io
ROAR is a shopping adviser for consumers. This app is of great benefit for all the segments i.e. business, consumers and retailers. You can click pictures of foods and drinks and instantly get the price, ingredients used in its preparation and see reviews from people who have tasted it.
Consumers can compare the same product from various brands for the price and they can also purchase the product right from the app, additionally, people go bonkers when they can buy movie tickets simply by scanning the movie poster. The app comes up with some great features in order to make the in-app experience fun. It is available on both iOS and Android.
Image Source:- youtube.com
Night Sky on iOS and Star Walk on Android, this app offers an amazing AR experience for stargazing and astronomy. The app uses your current location and through the mobile phone’s orientation provides an accurate star map based on the geo-location.
This map is usually designed to layer on top of the surrounding by using the AR functionality. This app is considered to be the best for learning and educating the astronomy.
Image Source:- theverge.com
Holo is an interactive app for both Android and iOS users allowing users to place various holographic figures into a scene for capturing video and pictures. The app has a limitation of only one holographic character per picture.
However, it is one of the most preferred AR apps because of its wide choice of preloaded content available. All your creations can be shared directly from the app to popular social media platforms.
Technology has taken over every walk of people’s lives in recent times either it’s discovering the most innovative mobile applications or creating best augmented reality games. So, it very well seems to be apparent that in near future Augmented Reality Apps will become an integral part of people’s lives. Hence it is wise to embrace the change it brings to our life as the main aim of Augmented Reality is to transform the world into a much better place for the coming generation.
Most of the AR apps are absolutely free which leaves no scope of ignoring this evolution. We encourage everyone to make the most use of these apps today and experience a different version of reality. Hope this best augmented reality apps article helps you to explore your knowledge.
Written By : Technostacks
Machine learning is a trending technology nowadays and it can be used in modern agriculture industry. The uses of ML in agriculture helps to create more healthy seeds.
The principle that Arthur Samuel used earlier in machine learning experiments are used in today’s modern agriculture. Artificial machine learning in agriculture is one of the fastest growing areas. Artificial techniques are being used in the agricultural sector to increase the accuracy and to find solutions to the problems.
Agriculture plays a very pivotal role in the global economy of the country. Due to the increase in population, there is constant pressure on the agricultural system to improve the productivity of the crops and to grow more crops.
In machine learning agriculture, the methods are derived from the learning process. These methodologies need to learn through experiences to perform a particular task. The ML consists of data that are based on a set of examples. An individual example is defined as a set of attributes. These sets of characteristics are known as variables or features. A feature can be represented as binary or numeric or ordinal. The performance of the machine learning is being calculated from the performance metric.
The performance of the ML model improves as it gains experience over time. To determine the performance of ML models and the machine learning algorithms agricultures various mathematical and statistical models are used. Once the learning process is completed, then the model can then be used to make an assumption, to classify and to test data. This is achieved after gaining the experience of the training process.
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It can be divided into two categories, namely supervised and unsupervised learning.
In this machine learning agriculture method, the input data is represented with examples to the corresponding outputs. The primary goal of this function is to create a rule that will map the inputs to the corresponding outputs. In some cases, the inputs might not be available that may lead to missing output. The trained model is then used in supervised learning to predict the disappeared production and then the data is being tested.
In this machine learning agriculture technique, there is no difference between the trained models and the test sets, while unlabeled data is being used. The goal of this method is to find the hidden patterns.
Machine learning is evolving along with big data technologies and other fast computing devices. They are growing to create new opportunities to understand the various data processes related to the environmental functions of agriculture. Machine learning can be defined as the scientific method that will allow machines the ability to learn without programming the devices. Machine learning is used in various scientific areas such as Bioinformatics, Biochemistry, Medicines, Meteorology, Economic Sciences, Robotics, Food Security and Climatology.
Artificial Intelligence is being used in various sectors from home to office and now in the agriculture sectors. Machine learning in agriculture used to improve the productivity and quality of the crops in the agriculture sector.
The seed retailers use this agriculture technology to churn the data to create better crops. While the pest control companies are using them to identify the various bacteria’s, bugs and vermins.
The AI technologies are used to determine which corn and which conditions will produce the best yield. It will also determine which weather condition will give the highest return.
One of the companies named Rentokil is using AI to kill all the bugs and vermin. Other companies are making use of Android app which is developed by Accenture to find bugs. The app takes the pictures of the bug and runs the app called as PestID. When a bug is found app will provide an immediate solution which helps the technician to take further actions. It will also recommend the chemical to be used to kill the bugs.
Let us look at the various applications of machine learning in agriculture.
Most of the companies are now programming and designing robots to handle the essential task related to agriculture. This includes harvesting crops and works faster than then human laborers. This is the best example of machine learning in agriculture.
Image Source:- yourvippartner.com
Companies are now making use of technologies and deep learning algorithms. The data are then collected using the drones and other software to monitor the crops and also the soil. They also use the software to control the fertility of the soil.
By making use of new technologies in agriculture, farmers can find effective ways to save their crop and also protect them from weeds. Companies are developing robots and automation tools to achieve them. Agricultural spray machines are designed, See and Spray robot that is being developed by Blue River Technology will monitor and spray accurate weeds on the plant like cotton. The precise amount of spraying can help to reduce herbicide expenditures.
Plant breeders are looking out for a particular trait on a regular basis. They look up for the qualities that will help the crops to use more water efficiently, use the nutrients and also adapt to the climate changes or any diseases. If the plant needs to give the desired result the scientist need to find the right gene. Find the correct sequence of the gene is difficult.
There is a rise in digital agriculture, which uses a secured approach to give maximum agricultural productivity by reducing the impact on the environment. The data that is generated in modern agriculture is based on various sensors that will help in better understanding of an environment like the crop, soil and the weather conditions and also about the agricultural machines. These data will help us to take quick and fast result-oriented decisions. To yield more, we need to apply machine learning to agriculture data.
A Mexico based Startup Company Descartes Labs are combining the satellite images, ML, Cloud computing and sensors to a better understanding of industries related to agriculture and energy. The company uses new technology in agriculture to discover where crops are situated and how good and healthy the crops are.
The machine learning tools which were reserved for some institutions are now accessible to all small and capable members. A small startup is making use if AI and machine learning to bring change in the modern agriculture sector. They are trying to reshape the contemporary agriculture sector by making use of innovative technologies.
If you are looking for progressive Machine Learning solutions, you have come to the precise place. We at Technostacks have the right capabilities to build clear-cut machine learning solutions that are supported by our in-depth acquaintance of industry applications, business-based services and the linked assortment of our diverse range of technologies.
Written By : Technostacks
Technostacks, reputed IT Company in India, has successfully carved its niche within a few years of its inception….