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Artificial Intelligence

How AI Can Help In Agriculture: Five Applications and Use Cases



AI in Agriculture

Artificial Intelligence (AI) is expanding its footprints at the ground level making a significant impact in the world’s most vital sector – Agriculture. After healthcare, automotive, manufacturing and finance sector now artificial intelligence in agriculture is providing cutting-edge technology for harvesting with better productivity and crop yield.

The Agriculture sector is the foundation of the world’s economy and with the increasing population, the world will need to produce 50% more food by 2050. AI-enabled technologies can help farmers get more from the land while using resources more sustainably. Here, we’ll learn how AI can be used in agriculture and its applications into farming.

AI Applications with Use Cases in Agriculture and Farming

ai in farming

Autonomous Tractors

With the heavy investment in developing autonomous vehicles for various needs, the agriculture sector will be also getting benefits with self-driving or you can say driverless tractors. With more quality training data for agriculture, the farm sector is going to be revolutionized by the large scale use of autonomous tractors for performing multiple tasks.

Video: Autonomous Tractor at Work

These self-driving or driverless tractors are programmed to independently detect their ploughing position into the fields or decide the speed and avoid obstacles like irrigation objects, humans and animals while performing various tasks.

Agricultural Robotics

Similarly, AI companies are developing robots that can easily perform multiple tasks in the farming field. Such robotics machines are trained to control weeds and harvest the crops at a much faster pace with higher volume compare to humans.

Video: AI Robots in Agriculture

These robots are well-trained to assist for checking the quality of crop and detect unwanted plants or weeds with picking and packing of crops at the same time capable to fight with other challenges faced by the agricultural labour force.

Companies like Blue River Technology and Harvest CROO Robotics are making such robotics machines that can control unwanted crops or weeds and help farmers in picking or packing of crops with higher volumes.

Controlling Pest Infestations

Pests are one of the worst enemies of the farmers damaging the crops globally before it is harvested and stored for human consumption. Popular insects like locusts, grasshoppers, and other insects are eating the profits of farmers and gobbling the grains meant for humans. But now AI in farming gives growers a weapon against such bugs.

ai in pest control

AI and data companies are helping farmers to get alert on his Smartphones about the grasshoppers likely to descend towards a particular farm. AI companies using the new satellite images against pictures of the same using historical data and AI algorithm detects that the insects had landed at another location and farmers use such information after confirmation and timely remove the costly pests from their fields.

Soil and Crops Health Monitoring

Continues deforestation and degradation of soil quality are becoming a big challenge for food producing countries. But now a German-based tech startup PEAT has developed a deep learning based application called Plantix that can identify the potential defects and nutrient deficiencies in the soil including plant pests and diseases. 

This app is working on image recognition based technology and you can use you your smartphone to capture the plant’s image and detect the defects into the plants. You will also get soil restoration techniques with tips and other solutions on short videos on this app.

Also Read: How Can Artificial Intelligence Benefit Humans?

Similarly, Trace Genomics is another machine learning based company provides soil analysis services to farmers. Such apps help farmers to monitor the soil and crop’s health conditions and produce a healthy crop with a higher level of productivity.

SkySquirrel Technologies acquired by another similar company VineView brought drone-based aerial imaging solutions for monitoring crops health. A drone is used to make a round of capturing the data from the vineyards field and then all the data is transferred via a USB drive from the drone to a computer and analyzed by the experts.

drone use in agriculture

The company uses the algorithms to analyze the captured images and provides a detailed report containing the current health of the vineyard, generally the condition of grapevine leaves as these plants are highly prone to grapevine diseases like molds and bacteria helping farmers to timely control using the pest control and other methods.  

Precision Farming with Predictive Analytics

AI applications in agriculture expanded into doing the accurate and controlled farming through providing proper guidance to farmers about optimum planting, water management, crop rotation, timely harvesting, nutrient management and pest attacks.

Video: What is Precision Farming?

While using the machine learning algorithms in connection with images captured by satellites and drones, AI-enabled technologies predict weather conditions, analyze crop sustainability and evaluate farms for the presence of diseases or pests and poor plant nutrition on farms with data like temperature, precipitation, wind speed, and solar radiation.

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

AI in agriculture not only helping farmers to automate their farming but also shifting to precise cultivation for higher crop yield and better quality while using less resources.

Companies involved in improving the machine learning or AI-based products or services like training data for agriculture, drone and automated machine making will get technological advancement in future will provide the more useful applications to this sector helping the world deal with food production issues for the growing population.

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Artificial Intelligence

How AI Is Creating New Job Opportunities For Low-Skilled Workers?



How AI Is Creating Jobs

As per the World Economic Forum’s job report, algorithms and intelligent machines are expected to create 133 million new roles globally while displacing around 75 million by 2022 – which is a total net gain of 58 million jobs, not killing them.

While, Gartner estimates AI will create 2.3 million new jobs in 2020 while eliminating 1.8 million positions. And according to Dun & Bradstreet 2019 report, 40% of organizations are adding more jobs, thanks to adopting AI, whereas only 8% are cutting jobs because of implementing such new technology into their operations and management.

However, according to Oxford academic duo Carl Benedikt Frey and Michael Osborne estimates published in the document “The Future of Employment: How Susceptible Are Jobs to Computerization?”, by the end of 2030 47% of American jobs are at high risk.

Artificial Intelligence (AI) based developments are on rapid speed – From self-driving cars, to robots removing weeds from farm fields or managing the inventory in assembly lines, warehouse or virtual assistant apps assisting people in solving their queries and computers able to detect cancer accurately is because of AI applied science.

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

Artificial Intelligence and machine learning oriented automation system will create, eliminate or change job roles, and how much is the question of discussion among the economic advisories and job data experts.

AI Creating New Jobs Rapidly for Highly-Skilled Professionals

Yes, its true AI is creating new job opportunities for highly-skilled professionals like software engineers, AI or machine learning engineer, data analyst, data scientists, digital communicators and online specialists. As these professionals are playing a key role in AI developments using their cognitive analytical and coding skills.

And as per the tops job sites and recruitment consultants, AI job postings as a percentage of overall job postings at on such websites rapidly increased and reached nearly doubled in the last two years. While searching the jobs in AI fields increased just 15 percent showing a huge gap between demand and availability of such professionals.

Reuters Graphic Showing Gap Between AI Job Openings and Job Seekers

Job growth rate in AI

This demand and supply gap between job openings and job seekers, is also pushing the pay scale bar of these professionals. As per the industry experts, the average salaries for AI-related jobs advertised by the companies on career sites rose 11% between October 2017 and September 2018 to $123,069 annually.

Will AI create jobs for low-skilled workers or not?

Highly-Skilled engineers and other professionals can easily find the jobs in AI fields, but the question arises here is that – automation and artificial intelligence will make low-skilled jobs disappear compare to these specialized and highly knowledgeable workers.

As per the job data in various countries, it is apparently visible that low-skilled workers are losing their job and becoming unemployed with lesser opportunists in job market due to rise of AI-oriented developments and implementation around the world.

Though, AI and automation have great scope of replacing repetitive and predictable cognitive and physical tasks. But there is a hidden side of AI and machine learning and that is rarely discussed by the experts, even most of the people not aware how AI is creating jobs not killing them even for low-skilled workers with great opportunities.

AI Creating New Jobs for Low-Skilled Workers

Data is the fuel requires to automated the AI and most of the machine learning algorithms need to be trained with huge volume of data sets. And training the machines with labeled datasets comes under the process of supervised learning.

In fact, a computer can take decisions or inferences, but only when you show enough examples with the respective solutions to individual problems. You can teach a neural network to recognize pictures of a car by feeding the network with thousands of images of a car while specifying every time to the algorithm.

The more pictures of cars you give, it will learn better and becomes more helpful in recognizing the images at a faster speed. And these AI and machine learning training data need to be annotated by someone – off course by humans.

Labeled Data Required for AI and Machine Learning

Humans-are-in-the-loop everywhere, in AI and ML development huge quantity of dataset, is required. And to generate the labeled data, thousands of human working hours required to annotate each image manually with precision.

AI and ML are escalating into vital fields, like healthcare and medical care. Automated pattern recognizing software is used in radiology, pathology, cardiology, oncology and even psychiatry helping doctors to detect different kind of diseases timely.

Also Read: How Does AI Work in Radiology: Applications and Use Cases

Medical imagining files like X-rays, CT Scans, MRI, ECGs and Ultrasound are manually annotated with the help of image annotation services highlighting pathological signals to doctors. And similarly, various other NLP based data for speech recognition are also highly in demand among the AI and ML developers for various industries.

A New Assembly Line for Low-skilled Workers

Though, there are various companies has developed software tools that are utilized by humans to annotate the different types of images. While many organizations have internal employees and many of them outsource the manual data labeling to others.

And such outsourcing is most probably done to underdeveloped or developing nations like India, China or African nations where the cost of labor is comparatively low. And these annotators are working in a big team, even some of the companies are working with more than 50,000 people, drawing from a pool of more than a million of annotators working worldwide in the day and night shifts producing the huge quantity annotated images.

Low-skilled workers basically work in manufacturing companies. But now companies become smarter implementing an autonomous or robotic system to perform the repetitive tasks with better speed and efficiency which were earlier performed by humans.

But right now in the age of rapidly growing AI development, data annotation is new assembly line providing the new opportunity to such workers. And these new types of jobs would not exist without machine learning algorithms which are at the revolutionary stage.

Data labeling is much more different from working in manufacturing assembly lines where workers perform physically exhausting demanding tasks. While in labeling data they need to more engaged in more cognitive tasks that are performed just sitting at one place on a chair in front of computers, it’s also repetitive but safe from machineries.

Data Annotation Doesn’t Require High-end Computer Skills

However, data annotation is not an easy job, as it requires training and meticulous attention to perform each task. You have to draw polygon or bounding box annotation around the various types of objects in an image or need to pinpoint the landmarks using the mouse and keyboards.

And while doing this you need to ensure the accuracy, because the quality of the data set is very important for the success of machine learning algorithm. In various industries like self-driving or autonomous driving, fallacious training data can be a cause of death due to crash or accidents becomes the prime reasons for fail of AI projects.

Also Read: Reasons Why AI and ML Projects Fail Due to Training Data Issues

Though, annotating the data is a time-consuming task, but it is very essential to teach the machines how to perceive various situations while running on the road and take precise decisions accordingly to avoid such disasters and providing the safe driving.

Job Opportunities for Both – Highly-skilled and Low-skilled

Envisaging the high volume characteristics of the tasks data annotation for AI creating jobs with great opportunity for low-skilled workers, especially for people living in developing or undeveloped countries where the job market is very low for unskilled group.

Many companies in such countries like Cogito are hiring the undergraduates, or fresh graduates and unskilled people training them creating jobs at large scale helping and improving the socio-economic  situation of the entire country.

Thanks to AI, not only highly-skilled professionals, but unskilled people are now also finding the jobs easily with satisfying pay scale fulfilling their basic needs. Further with more developments in unexploited fields AI and ML will create more jobs for low-skilled workers which accounts for a major population of many countries around the world.

Is Data Annotation Job Good for them?

The other side of this story is that, instead of learning more skill-based knowledge, such employees stuck in low-skilled jobs, which is economically not good for their long-term growth and developments.

But companies and organizations, in an attempt to operate in the market with hyper-competitive pricing with higher margins, are keeping annotators salary tremendously low as much as below $1 per hour which is below minimum wage.

Actually, in the digital era, such organizations are adopting this line of business which also sponsoring a new kind of slavery in the digital era. And the hunger of data annotation is so big, that in short-term this kind approach will be monetarily more rewarding for the companies compare to other lines of business.

While from the long-term perspective, it will affect the whole economy, as it will determine high employee churn rate, bad quality in the output, and negative impact on various communities.

Such companies, not only upsetting social norms by exploiting workers and running the business unethically, but this kind of unfair business practice is also harmful to the whole industry across the world will also impact the AI sector.


Though, AI is already a controversial technology, due to lots of societal, ethical and moral concerns associated with its developments. And this kind of dirty games by corporates can cost them when it will affect the entire sector with cascading effects.

Also Read: How AI Training Data Can Be A Security Threat To Your Company?

In the nutshell, the human race of automation with AI is eliminating and creating millions of jobs worldwide in various sectors. And it is also not necessarily true that all the jobs created by AI are only for high-skilled and knowledgeable professionals but low-skilled workers are also getting new opportunities with multiple job options.

Although, the rate at which such jobs are created might not match the rate at which other low-skilled positions are disappearing globally. AI is at the growing stage, and the availability of annotated data and the need to access such data sets will grow exponentially over the next years, means a steep rise in demand for data annotators.

Also Read: How Can Artificial Intelligence Benefit Humans?

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Artificial Intelligence

How AI Training Data Can Be A Security Threat To Your Company?



AI training data risk and threat

The race into Artificial Intelligence (AI) flying field is becoming more expeditious with new and more innovative developments across the world. However, such competitive approach also posing a new threat to companies working on such projects.

Every coin has two sides – AI is also brining such risk which needs to understand timely. Actually, I’m talking about the training data that is primarily used in model training and development. Though, there are many security issues with AI-based model development and applications developed using this technology.

But training data is the information you upload into the algorithm to create your AI model is highly vulnerable towards the hackers and fraudsters, if they manage to access that. If such crucial data landed into wrong hands, it can be mischievously manipulated to crash your computer networking system or breach the privacy and security of your company.

Your AI-based application is also equally susceptible to hacks or data leaks, hence you need to protect your data and we have discussed right here what are the major issues you need to consider while working on such AI-based projects.

Are you storing your data at a safe place?

If you are using cloud-based AI services where you upload your labeled data on such online data servers. You don’t know if this cloud is directly under control of your service provider, or do they own the entire stack. Moreover, always remember that maybe your cloud is secured, but not necessary it is used in a secured manner.

As per the global research and advisory firm, Gartner’s prediction up to 2022, at least 65% of cloud security failures will be occurred due to customer’s fault.

Hence, you need to make sure use the secured cloud-based AI services and also need to audit threats and risks that your employees usage may pose to the cloud.

Who possess your training data?

This is a big question – Who owns your training data? Actually, when you use a cloud-based machine learning algorithm to develop an AI-based model, you have to upload your training or testing data into the services algorithm.

Also Read: What is Training and Testing Data in Machine Learning with Types?

Let’s take an example – Suppose you are using an NLP look through your call data which a proprietary data providing a competitive edge is a very sensitive data that you are legally bound to protect from unauthorized users.

Here, you need to make sure you do not permit any rights to your data and that you will own the final model created with that data. So, carefully check the “terms of service”, license agreements and other fine print before signing up for using any cloud-based AI technology.

And if you find anything unfavorable in their “terms of service” or ambiguous you can terminate the service and go with other reliable service providers.

Similarly, you also need to make sure you are not using unmoderated publicly available datasets for your AI or ML training data, as this can open you up to malevolent data source that can seriously poison your application or fail the model.

Also Read: Reasons Why AI and ML Projects Fail Due to Training Data Issues

Do you clean up the data from such servers?

Once your AI model training is completed, it is necessary to delete all vestige from the servers used in training. Finally, you are going to use the AI application and whenever you kick-off your AI project, it’s easy to carry off with the possibilities it opens out.

Usually, managers emphasize speed and innovation, but take your time to understand what is going on behind the scenes. This will definitely help you in long-run in securing your application from mishandling while ensuring the privacy of the customer’s data.

Acquiring the machine learning training data is not difficult but keeping it secured and private during the model development is very important for every company enthusiastic in such developments.

Thus, consider such aspects while working on such projects, especially when you use a cloud-based algorithm to build an AI model and stay ahead in the competitive market without losing your privacy and data safety.

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Artificial Intelligence

Reasons Why AI and ML Projects Fail Due to Training Data Issues



Why AI & ML Projects Fail

Artificial Intelligence (AI) market is posing to become billions of dollar industry in next few years, as global spending by nations on AI is likely to touch around $35.8 billion in 2029 which reports a growth of 44% over the amount spent in year 2018.

Such, impressive growth shows, AI holds huge potential to attract big organizations as well small enterprises attracting them to implement AI-enabled services for better growth in the business. However, working with AI you need immense amount of meticulous data to train the model so that it can give the precise results.

Actually, to train an AI or ML model a high-quality training data is required, which is a challenging task for AI developers or machine learning engineers. As, to get the human like complex decisions from machines you need enormous volumes of accurately labeled and annotated training data through images or videos.

With the growing AI demand, data science team are under pressure to complete the projects but acquiring the training data at a large scale is the real challenge they are facing right now.

Why Do Enterprises Face Data Issue for AI Strategy?

As per the research by Dimensional Research and Aiegion survey, enterprise machine learning is just beginning, machine learning engineers or data scientist team size is smaller and the expertise of growing data science is not yet compatible to matured ML projects expertize.

And acquiring the training data is the biggest challenge for the success of an AI project. As per the survey, 96% of the AI projects fail or not started due to lack of training data technology that leads to the inability to train the ML algorithms resulting failure of the project.

Half of the AI Projects Never Get Deployed

Nowadays, big organizations or enterprises having more than 100,000 employees are more keen to implement AI strategy into their business model – but only 50% of such enterprises currently have one. The survey reinforces that AI is at nascent in the enterprise, as 70% of them firstly invested in AI/ML projects in the last 24 months.

While on the other hand, over half of the enterprises report they have undertaken fewer than four AI and ML projects. And only half of the enterprises have released AI/ML projects into the development to build a fully-functional model.

Also Read: What is Training and Testing Data in Machine Learning with Types?

And as per the survey research only, less than two-thirds of them indicated that their ML project reached the completion point that is being trained on labeled training data sets which are relatively at the initial stage in the ML project life cycle. And more revealing immaturity of ML in the enterprise, is that why half of the projects never deployed.

Survey Statistic Why AI/ML Projects Fail:

  • 78% of AI/ML Projects Shut ate some stage Before Deployment
  • 81% Admit the process of training AI with data is more difficult than they expected
  • 76% struggle by attempting to label or annotate the training data on their own.
  • 63% try to build their own labeling and annotation automation technology.

And as per the research, around 40% of failed projects reportedly stalled during training data-intensive phases like training data preparation, algorithms training model validation, scoring and post-deployment enhancement.

Top Reasons for AI Projects Failure:

  • Lack of Expertise (55%)
  • Unexpected Complications (55%)
  • Training Data Problems (36%)
  • Lack of Model Certainty (29%)
  • Deficient Budget (26%), and
  • Lack of Efficient Staff (23%)

As already bespeak, around two-thirds report that ML projects not able to progressed beyond proof of concept and algorithms development to the phase of training data. Mostly this phase is not favorable for such developments, as 80% report that training the algorithms is more challenging than the AI engineers have expected.

Reasons Why Training Algorithms Data is Challenging:

  • Not enough data
  • Data not in a usable form
  • Bias or errors in the data
  • Don’t have the tools to label the data
  • Don’t have the people to label data

Nevertheless, less than 4% have reported that training data has presented without any problems. Almost three-quarters of the AI engineers indicated that they try to label and annotate training data on their own. While around 40% suggested they rely wholly or partially on off-the-shelf, pre-labeled data to train their AI model.

Such issues, lead to 70% companies utilizing external services for their AI or ML projects with most of them focusing on data collection, labeling and annotations. As AI and ML engineers are rare to find and also expensive, the enterprise should find out external solution service providers for critical activities like data labeling and model scoring. This evidence is enough to outsource data annotation for more improved outcomes.


Enterprises designate a strategic value to their machine learning initiatives and expect AI and ML shall improve their businesses aspects and would be also disruptive in their sectors.

However, AI and ML projects are still at an early stage of development at enterprises. And data science and AI engineer teams are relatively small and experienced which affects the efficiency and outcome of these projects.

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