Applications Of Robots in Agriculture

Technology has transformed so many industries, and agriculture isn’t an exception here. With the help of technology, agriculture is evolving as a high-tech job lately with so many robots and drones involved in it. The engineers are striving and working hard to design the robotic models that stand out and help farmers in their various applications.

There are renowned companies that are manufacturing agriculture-related robotics that is taking care of various tasks of a farmer. The robot market is ever-growing, and with every passing year, the new technology is making a huge impact.

As per the statista report, The market value of social and entertainment robots was 1.08 billion USD in 2019 and it is expected to reach 1.38 billion USD by 2025. The market value for robots has skyrocketed lately, and now the reported CAGR for them is around 23%. This scenario will increase and reach even more heights in 2020, and by 2023 it is expected to become a 12 billion USD market around the world as per the agriculture robot research paper.

The development of robotic applications in the agriculture industry is pretty quick, and with fantastic results, it is turning to be one of the biggest revolutions in agriculture pretty soon.

Which Are The Top Agriculture Robotics Applications Can be Used In Farming?

Here are some of the amazing robotic applications in the agricultural industry that can potentially transform the market. Let’s explore the ways robotics is changing the agriculture industry.

1. Harvesting and Picking

Harvesting is a repetitive work that takes up substantial manual labor and becomes a little boring after some while. This scenario is a task that should be replaced by robots so that the work gets done in time correctly. However, that’s not the case all the time. Harvesting particular kinds of plants like wheat, barley, and corn are natural, but when it comes to some other products like fruits and vegetables, it is not possible to harvest them. The farmers can use combined harvester, which will work as a tractor and harvest the plants, but the same strategy isn’t fit for fruits and various other plants.

Some companies are trying to prepare a robot that can harvest fruits without any hassle, but they haven’t found a reliable solution yet. If the technology is successful, then it will evolve as one of the most critical things in the agricultural robots future.

2. Crop Seeding

Every plant goes from a seed, and it is essential to spread seeds in the right places around the field. The traditional method of sowing seeds is to distribute them across the field with the help of a tractor. The farmers attach a broadcast spreader to the tractor, and this will scatter the seeds while the tractor is driven at a steady pace. This method is not very efficient and has some of the disadvantages too.

The autonomous precision seeding is a technology that is used for the spreading of seeds. It uses geo-mapping technology to find the precise points where the soil quality is excellent and will help in distributing seeds there only. This way, it becomes pretty easy for the farmers to complete seeding in the places where there are better prospects. The placement of these seeds is going to matter a lot as it will have a massive influence on agriculture performance.

3. Fertilization and Irrigation

Fertilization and irrigation are two pretty essential as well as severe tasks for farmers. If irrigation is not done effectively, it will waste the needed amount of water, which is not something one can afford to do. The same goes for fertilizer too. If proper fertilization methods are not adopted, the farmers end up wasting a lot of fertilizer as it becomes tough to go into the grown field. This scenario is the reason why it is always a good thing to rely on robotics to do these things.

The robots are pretty advanced, and they understand how much water a particular kind of plant needs and provide them with enough water only. The similar case applies for fertilizer too. Every plant will get the right amount of fertilizer that will keep it safe. They are small in size and can go into fields like corn plants pretty quickly. This way, the work can be completed with ease, and the farmers don’t have to worry about improper watering or fertilization.

4. Thinning and Pruning

Both thinning and pruning are vital tasks that need special care as well as precision. Thinning is a process where the unwanted plants or parts of plants are removed so that they can make room for the new plants. This process is pretty tough, and if farmers don’t pay proper attention, they may harm an entire tree too. This case is the reason why it is crucial to go with the robotics that will help in cutting the trees entirely. The robot will help in the density reduction of the plants. When you leave a robot in the field, it will examine each plant and identify the parts that they have to remove and the products that they have to keep safe.

Pruning is a pretty complicated job where the farmers have to remove particular parts of the plant so that the tree grows effectively. If precise care is not taken while removing the parts of plants, it can result in some huge problems. The latest robotic automation process will help in getting rid of the plant parts that are not essential for a tree and helps it grow even more effectively.

5. Nursery Planting

Nurseries have always been a go-to option for so many gardeners. And people who are interested in growing the plants in their home. The nurseries are in need of a robotic automation process so that the entire work is executed without human intervention. This way, the planting process won’t require manual labor, and a lot of efforts will be saved at the same time.

6. Monitoring of Crops and Analysis

It is crucial to monitor the agricultural fields from time to time in order to curb any kind of plant issues in it. A human can’t get into the field and check each and every plant with the utmost precision. There are high chances of human error, and this is where the robotic automation steps in the process. The monitoring of fields is one of the essential agricultural robotics applications in this industry, and it is going to have a considerable impact.

There are drones and various other kinds of robots that will observe every plant present in a field irrespective of its size. These robots will then help in identifying any type of problems or issues and provide its report to the farmers directly. This way, it becomes pretty easy for farmers to know what kind of issues are there in their fields without going and checking them by themselves. The precision and perfection of these robots are noteworthy.

7. Spraying Pesticides and Weeding of Crops

Pesticides are pretty harmful to the environment, and spraying more amounts of them onto the plants can harm the people who are consuming it. Usually, when farmers spray these pesticides, there are high chances of them spraying more than usual and harm the environment. This scenario is the critical reason why automation is significant in this particular field. The robots will distribute an equal amount of pesticide for every plant and ensure that an extra amount of pesticide isn’t distributed.

The same goes for the weeding of crops too. There are robots that can be attached to tractors and taken into the field. These robots will check the plants, and if there are any weeds, they are taken out immediately without harming the neighbor plants. These robots implement techniques and use tools such as a laser for getting rid of weeds.

How Can I Implement Digital Robotic Technology In My Farming?

These are the ways and approaches how robotics can help in farming as well as the agriculture sector. Technostacks is a leading app development company and has been working towards the goal of digitizing the farming methodologies. We have worked and delivered app solutions for the harvesting and agriculture business already.

If you want us to convert your agriculture farming strategies into current digital technology, then we are here to solve your problems. Also, if you have a project idea, you can any time get in touch with us for practical and cost-effective solutions to bring in the great future of robotic agriculture.

Written By : Technostacks
benefits of ai in business

In the past few years, Artificial intelligence has gained a lot of momentum in various industries. Industries like healthcare, retail, logistics, manufacturing, transportation have started using artificial intelligence based applications in order to improve productivity and performance.

Artificial Intelligence hasn’t been completely incorporated in the businesses yet, but the companies and its employees are using these techniques without even knowing about them.

Machine learning and AI for business are one of the most important aspects in the present day. Both technologies are making a huge difference in the ways that operate their businesses.

According to a report given by Forbes, artificial intelligence will contribute more than $15.3 trillion to the global economy by the end of 2030. This helps the businesses to reap huge benefits with these technologies. In a study conducted by MIT, it has been proved that more than 85% of the executives believe that there is a plethora of benefits of AI in business growth.

The best way to evaluate a technology is by understanding the benefits of that particular technology in your business. Here are some of the benefits of artificial intelligence in business.

1. Automate Your Marketing Techniques to Improve the Sales

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These days, digital marketing is the most imperative aspect of your online business. It is very significant for businesses to use various marketing techniques to get better prospects and convert them to loyal customers. Incorporating the AI into this marketing will help the organization in many ways. The AI-based applications can handle the routine tasks and they can customize the sales and marketing information depending upon the consumers.

The AI chatbots can be considered as another boon for the businesses in this digital era. These chatbots are helping in keeping the customer engaged and solves their doubts without any need for a customer service executive.

The customers who usually visit the website with a query won’t have the time to wait until one of your customer service executives shows up and in such instances; the AI chatbots do a miracle. They not only provide customers with their answers, but advanced technology helps in interacting with the customers and providing them with extreme customer satisfaction.

The machine learning can also help in optimizing the price of various markets. A data science platform, Rapid Miner leverages the data about different competitors, consumer preferences, suppliers, and risks to create the pricing models for the individual market segments automatically. This AI-based approach will help the businesses to optimize the marginal profits.

2. AI Based Analytics for Better Business Decisions

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The modifications in the recent networking and storage technology have given rise to the age of big data. But what can one do with the analytics if they don’t have proper ways to analyze the information? Due to the large size, the human intelligence isn’t enough to analyze the data. The technology should be used to make the better business decisions.

The machine learning and deep algorithms will help in this analysis. SAP’s in memory data platform named HANA is using the machine learning to analyze the big data and create patterns according to it.

Walmart has been using this platform for the data analytics. More than 245 million customers visit Walmart stores and websites all over the world. So, the data that this company collects is enormous and without a proper intelligence technology, they won’t be able to analyze and use this data. The HANA’s machine learning algorithms will bring important data to the forefront so that the Walmart employees can make informed data driven decisions.

3. Enhances Both Security and Maintenance of Your Equipment

AI For Cybersecurity

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The artificial intelligence can improve the maintenance schedules in the manufacturing and transportation sectors. Let’s take an example of airlines industry; the industry always tries to predict wear and tear of the mechanical parts of their fleet in order to prevent the downtimes. The AI-based predictive analysis will help in improvising this process dramatically. The airline’s industry will be able to create more optimized maintenance schedules with the help of Artificial intelligence.

The manufacturing industry has been using the AI for the maintenance and safety for a long time. The General Electric has developed a Predix Platform which will use Artificial intelligence to optimize and scale the industrial applications.

4. Saving Time

AI time saver

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No matter what type of business you are in, the time plays a prominent role there. In the business world “Time is Money” and one cannot simply waste their time with simple yet time consuming tasks like data analytics.

Analyzing the data with human intelligence will take a lot of time and this is the reason why people prefer to use the help of artificial intelligence. The artificial intelligence will help in the entire process and it will save a lot of time too. The situation is similar with chat bots too. One cannot spend their entire time before a computer waiting for the questions to pop from the customers. The artificial intelligence will reduce human efforts and saves their time in this industry too.

5. Easing the Inventory and Supply Chain Management

AI Supply Chain Management

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The machine learning algorithms will help both retail and other businesses with better management of their inventory. It is capable to automate the refilling requests and helps in the optimization of the supply chain. You can just hand over the maintenance of both inventory and supply chain to artificial intelligence. This is one of the ways AI can help the business.

Major AI based companies like IBM Watson are investing in this supply chain and inventory management at a large sum. IBM’s supply chain management will help in automating your order fulfillment and management. There is another company named “Transvoyant” which is combining the Internet of Things and machine learning to create applications that will predict the supply chain movements.

6. Advanced Hiring Processes

AI Hiring

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It is one of the common issues in any of the organization. Searching and hiring the right candidate isn’t something that can be just done. It takes a lot of cumbersome procedures to work. But with the latest AI based facial recognition applications, the interviewing and hiring process has become easy. This technology will evaluate the performance of an employee by their emotional cues which will help businesses to streamline their processes.

Various giant companies like IBM, Dunkin Donuts and Unilever have already started using artificial intelligence to screen the entry-level employees. Unilever has already declared how AI has aided them in the hiring process. The Applicants can give interviews using HireVue app on the smartphone. This app will use the video and audio data of the interview and analyzes it and on top of that provides the recommendations for the next step of the human recruiters.

7. AI helps in Fighting Frauds and Preventing Crimes

ai fraud detection

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The businesses spend a lot of time on detecting the fraudulent transactions, but it is like a never-ending process for the businesses. Usually, the frauds and crimes can be detected depending upon the pattern recognition. The machine learning tools can take care of this pattern recognition. Like this the cyber threats are also dependent on recognizing the pattern anomalies and AI-based applications will help in this area too.

A company named is using the latest machine learning techniques to develop the solutions that will help the company in detecting the frauds in a better manner. The rule-based systems detect a lot of positive false alarms and the machine learning system can detect such false alarms.

Key Takeaways

These are some of the best ways to use the power of Artificial Intelligence in different businesses. If someone asks you about how AI can help your business, you can provide them with these benefits and added advantages. There are enormous paybacks by incorporating AI in a business and most of the companies are using it to the fullest.

Are you want to implement AI in your business, then you can contact us. We at Technostacks Infotech expert in developing AI-based application. We will give the best assistance for your organization.

Written By : Technostacks
best machine learning frameworks

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.

1. TensorFlow


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.

2. Caffe


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.

3. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit

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.

4. Torch


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.

5. MXNet


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.

6. Chainer


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.

7. Keras


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.

Key Takeaways

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
Machine learning in agriculture

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.

A) Machine Learning Methods

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.

agriculture with Machine Learning

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Machine Learning Functions

It can be divided into two categories, namely supervised and unsupervised learning.

  • Supervised 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.

  • Unsupervised Learning
  • 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.

B) The Machine Learning (ML) Evolution in Different Areas

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.

C) Uses of Machine Learning (ML) in Agriculture

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.

  • Retailers
  • 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.

  • AI is used to boost the yield of crops
  • 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.

  • AI helps to identify bug hunters
  • 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.

D) Most Popular Applications of Machine Learning (ML) in Agriculture

Let us look at the various applications of machine learning in agriculture.

  • Agriculture Robot
  • 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.

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  • Monitor crop and soil
  • 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.

E) Machine Learning (ML) Models Used in the Agriculture Industry

  • The agricultural farmers are now taking advantage of the machine learning models and their innovations. Using AI and machine learning is good for the food tech segments.
  • The Farmers Business Network that is being created for the farmers a social network will make use of the ML and the analytic tools to drive the results of data on pricing.
  • Robots are now managing the crops and also monitoring them.
  • Sensors are helping to collect the data related to crops.
  • According to research if AI and ML are being used in agriculture, then the agriculture sector will grow in the coming years.

F) Rising Opportunities of Machine Learning (ML) in Digital Agriculture

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.

G) Real-life ML Example

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.

Moving Forward

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
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