what are the two recent themes for new ways to use old crops?

This article was published equally a part of the Data Scientific discipline Blogathon.

Overview

• Lifecycle of agriculture
• Challenges faced in Agriculture with traditional farming techniques.

Artificial Intelligence

Bogus intelligence is based on the principle that human intelligence can be defined in a way that a auto can easily mimic it and execute tasks, from the simplest to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.

"We're at get-go of a gold age of AI. Recent advancements have already led to invention that previously lived in the realm of science fiction – and nosotros have just scratched the surface of what's possible"
– JEFF BEZOS, Amazon CEO

Some examples, vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past, speech, and language recognition of the Siri virtual assistant on the Apple tree iPhone.

AI is making a huge impact in all domains of the industry. Every industry looking to automate certain jobs through the use of intelligent mechanism. And a adept Artificial Intelligence Grade Online is all you demand to break into whatever manufacture. Fifty-fifty Agriculture!

Agriculture and farming are i of the oldest and most of import professions in the world. It plays an important office in the economic sector. Worldwide, agriculture is a $v trillion industry.

The global population is expected to attain more than nine billion by 2050 which will require an increase in agricultural production past 70% to fulfill the demand. As the globe population is increasing due to which land water and resources becoming insufficient to go along the demand-supply chain. And so, nosotros need a smarter approach and become more efficient about how we farm and can be most productive

In this article, I will cover are challenges faced by farmers past using traditional methods of farming and how Artificial Intelligence is making a revolution in agriculture by replacing traditional methods by using more than efficient methods and helping the globe to become a better identify.

Lifecycle of Agriculture

Nosotros can divide the Procedure of Agriculture into different parts:

Training of soil: It is the initial stage of farming where farmers prepare the soil for sowing seeds. This process involves breaking large soil clumps and remove droppings, such every bit sticks, rocks, and roots. Also, add together fertilizers and organic thing depend on the type of crop to create an ideal state of affairs for crops.

Sowing of seeds: This stage requires taking care of the distance betwixt two seeds, depth for planting seeds. At this phase climatic weather such as temperature, humidity, and rainfall play an important role.

Calculation Fertilizers: To maintain soil fertility is an of import factor so the farmer tin can continue to grow nutritious crops and healthy crops. Farmers plough to fertilizers considering these substances comprise plant nutrients such equally nitrogen, phosphorus, and potassium. Fertilizers are just planted nutrients practical to agricultural fields to supplement the required elements plant naturally in the soil. This stage likewise determines the quality of the crop

Irrigation: This stage helps to keep the soil moist and maintain humidity. Underwatering or overwatering can hamper the growth of crops and if not washed properly information technology tin can atomic number 82 to damaged crops.

Weed protection: Weeds are unwanted plants that grow near crops or at the boundary of farms. Weed protection is important to factor as weed decreases yields, increases production cost, interfere with harvest, and lower crop quality

Harvesting: It is the process of gathering ripe crops from the fields. Information technology requires a lot of laborers for this action so this is a labor-intensive activity. This phase also includes mail-harvest treatment such as cleaning, sorting, packing, and cooling.

Storage: This phase of the post-harvest system during which the products are kept in such a mode as to guarantee food security other than during periods of agriculture. Information technology also includes packing and transportation of crops.

Challenges faced by farmers by using traditional methods of farming

Listing downwardly full general challenges that be in the agricultural domain.

o In farming climatic factors such as rainfall, temperature and humidity play an important role in the agriculture lifecycle. Increasing deforestation and pollution issue in climatic changes, and then information technology'southward difficult for farmers to take decisions to set the soil, sow seeds, and harvest.

o Every crop requires specific nutrition in the soil. There are 3 main nutrients nitrogen(Due north), phosphorous(P) and potassium(Grand) required in soil. The deficiency of nutrients tin lead to poor quality of crops.

o As nosotros can see from the agronomics lifecycle that weed protection plays an important role. If not controlled it can lead to an increase in production cost and also information technology absorbs nutrients from the soil which tin cause diet deficiency in the soil.

Applications of Artificial Intelligence in Agriculture

The manufacture is turning to Artificial Intelligence technologies to aid yield healthier crops, control pests, monitor soil, and growing conditions, organize information for farmers, aid with the workload, and improve a wide range of agriculture-related tasks in the entire food supply concatenation.

Use of weather forecasting: With the change in climatic condition and increasing pollution information technology's difficult for farmers to decide the right time for sowing seed, with aid of Bogus Intelligence farmers can analyze weather conditions by using atmospheric condition forecasting which helps they program the type of ingather can be grown and when should seeds be sown.

Soil and crop health monitoring organization: The type of soil and nutrition of soil plays an of import factor in the type of crop is grown and the quality of the crop. Due to increasing, deforestation soil quality is degrading and it's hard to determine the quality of the soil.

A German-based tech start-up PEAT has developed an AI-based application called Plantix that can place the nutrient deficiencies in soil including plant pests and diseases by which farmers can also get an idea to use fertilizer which helps to improve harvest quality. This app uses image recognition-based engineering science. The farmer tin capture images of plants using smartphones. We can also run across soil restoration techniques with tips and other solutions through short videos on this application.

Similarly, Trace Genomics is another machine learning-based company that helps farmers to practise a soil assay to farmers. Such type of app helps farmers to monitor soil and ingather's health conditions and produce healthy crops with a higher level of productivity.

Analyzing  crop health by drones: SkySqurrel Technologies has brought drone-based Ariel imaging solutions for monitoring crop health. In this technique, the drone captures data from fields and then data is transferred via a USB drive from the drone to a computer and analyzed by experts.

This visitor uses algorithms to analyze the captured images and provide a detailed report containing the current wellness of the farm. It helps the farmer to identify pests and bacteria helping farmers to timely apply of pest control and other methods to accept required action

Precision Farming and Predictive Analytics: AI applications in agronomics have adult applications and tools which assist farmers inaccurate and controlled farming by providing them proper guidance to farmers about water management, crop rotation, timely harvesting, type of crop to be grown, optimum planting, pest attacks, nutrition management.

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

Farmers without connectivity can go AI benefits correct now, with tools as simple equally an SMS-enabled telephone and the Sowing App. Meanwhile, farmers with Wi-Fi access can use AI applications to get a continually AI-customized plan for their lands. With such IoT- and AI-driven solutions, farmers can meet the world's needs for increased nutrient sustainably growing production and revenues without depleting precious natural resources.

In the time to come, AI will help farmers evolve into agronomical technologists, using information to optimize yields downward to individual rows of plants

Agronomical Robotics: AI companies are developing robots that tin hands perform multiple tasks in farming fields. This type of robot is trained to command weeds and harvest crops at a faster step with higher volumes compared to humans.

These types of robots are trained to check the quality of crops and discover weed with picking and packing of crops at the same time. These robots are also capable to fight with challenges faced past agronomical forcefulness labor.

AI-enabled system to detect pests: Pests are i of the worst enemies of the farmers which damages crops.

AI systems use satellite images and compare them with historical data using AI algorithms and discover that if whatsoever insect has landed and which type of insect has landed like the locust, grasshopper, etc. And transport alerts to farmers to their smartphones so that farmers tin can take required precautions and use required pest control thus AI helps farmers to fight confronting pests.

Conclusion

Artificial Intelligence in agriculture non only helping farmers to automate their farming just also shifts to precise cultivation for higher ingather yield and meliorate quality while using fewer resources.

Companies involved in improving automobile learning or Artificial Intelligence-based products or services like training data for agronomics, drone, and automatic machine making will become technological advocacy in the future will provide more useful applications to this sector helping the world deal with food production issues for the growing population.

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Source: https://www.analyticsvidhya.com/blog/2020/11/artificial-intelligence-in-agriculture-using-modern-day-ai-to-solve-traditional-farming-problems/

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