Farming is an intensive sector with weather changes, soil deterioration, and low crop production challenges. However, modern technologies like artificial intelligence(AI) are helping farmers improve yields through advanced analytics.

So, what’s so great about using AI in agriculture?

AI is solving one of the significant issues farmers face—“Pests!

Yes! Pests and insects eat crops, reducing yield.

AI helps companies reduce the impact of pests by identifying specific patterns using multiple cameras and sensors. These systems continuously monitor fields and crops for signs of pests or diseases. Similarly, startups worldwide have identified many uses of AI in agriculture.

More than 200 startups in the US are using AI in the agriculture sector. Indoor-farming businesses like Plenty and AppHarvest use AI and computer vision to optimize the environment around the crops. However, there are challenges to AI adoption in agriculture.

This article focuses on AI’s impact on agriculture, including its benefits, challenges, and solutions.

What is AI in Agriculture?

AI in agriculture is an approach to integrating artificial intelligence algorithms into farming processes to improve crop yield, reduce cost, and automate tasks. Agriculture technology companies can leverage custom AI models to make farming efficient, creating an ecosystem of intelligent equipment.

This includes,

  • A series of cameras
  • Multiple drones
  • Sensors
  • Digital platform or software
  • Robust data infrastructure

Now that you know what is AI in agriculture, it’s time to understand its impact.

Impact of AI on Agriculture

Conventional farming processes involve rigorous physical activities from farmers, which often go to waste due to factors that humans can’t control.

Weather, for that matter, is not a controllable factor. However, if advanced technology like AI is paired with enough weather data, the impact of weather on crops can be minimized.

While the conventional approach made detecting pests, diseases, and harmful insects that damage crops difficult, AI is making it possible.

Modern farming startups are now using drones with multiple cameras to capture visuals. These visuals are then used with deep learning algorithms and computer vision to identify pests and diseases in crops.

Besides disease detection in crops, AI helps manufacturers create innovative farming equipment. For example, John Deere uses AI-powered see and spray technology to differentiate plants from weeds and treat individual crops, reducing pesticide waste.

Implementing AI technology in agriculture has a significant impact with improved crop yield, reduced wastage, and decreased farming costs.

So, what’s driving AI adoption in farming?

Here are some key drivers boosting AI use in agriculture.

Key Drivers Behind the Adoption of AI in Farming

  • The agricultural sector’s labor shortage is critical to AI’s use. It helps automate several farming activities.
  • Need for precision farming practices to improve sustainability is driving AI use in agriculture.
  • Identifying weather patterns and reducing their impact needs data analytics.
  • The data volume is larger, with information ranging from satellite images to sensor data recorded on farming equipment.
  • Using AI in agriculture technologies makes the analysis and processing of data efficient.

Knowing the impact is not enough, as you need to invest heavily upfront in implementing AI in agriculture. Making that decision will be easy if you know what challenges AI can solve.

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How AI in Agritech Solves Prominent Farming Challenges?

Like all other domains, agriculture faces specific challenges, such as supply chain disruptions, skill shortages, and higher cost-to-profit ratios.

Challenge 1: Disrupted Supply Chains

A significant issue for farmers since the pandemic has been disrupted supply chains. Due to supply chain disruptions, farmers face a shortage of packing materials, leading to damaged crops. According to USDA, 3% of food damage in North America is due to packaging issues.

how ai in agritech solves prominent farming challenges

AI in Agricultural Optimization

Agriculture tech firms and farmers can leverage AI algorithms for advanced demand forecasting. It helps farmers predict the need for packaging materials, reducing shortages.

Challenge 2: Shortage of Farming Labor

The farm labor shortage is a significant concern in the US. More than 2.4 million vacant farm jobs indicate farm owners suffer from a substantial labor crunch.

AI Solutions for Agriculture

Farm owners can leverage AI farming technology to automate tasks and reduce dependency on farm laborers. Some examples of artificial intelligence in agriculture that helps in farming automation are driverless tractors, smart irrigation, auto-spraying drones, fertilizing systems, and harvesting bots.

Challenge 3: Cost-to-profit Ratio

The operating profit margin for the farm sector in the US is 0.16, which has declined from 0.22 in 2023 and 0.24 in 2022. This means that margins are low, and ROI for farmers is declining. Higher capital investment and unpredictable yields are causing higher costs and lower margins.

AI Use in Agriculture

Using artificial intelligence in farming can drastically reduce costs, improving profit margins. AI-based technologies can help reduce the cost of labor through automation. Another key benefit of AI in agriculture is reduced wastage and high crop yield. AI also helps in targeted fertilizer spraying, lowering the cost of fertilizer waste.

AI offers solutions to most farming challenges, but its advantages go beyond the usual benefits of automation.

6 Major Advantages of AI in agriculture

advantages of ai in agriculture
If you are an agritech company, what are you looking for in an AI system?

Better cost efficiency? Automation or reduced wastage or better profit?

Your objective defines the benefits of AI in farming you can expect. For example, if you want to reduce the wastage of water, fertilizers, and pesticides on your farm, AI offers precision farming.

1. Predictive Analytics

Use of AI in agriculture can help predict which crops can be grown in specific scenarios. It can analyze weather patterns and respective crop yields to help establish a correlation.

Based on the co-relation, it recommends which crop is best for maximum production in the current weather conditions. Using AI solutions in agriculture, you can analyze and categorize which crops provide more profits.

This will help you determine which crops are best for your farm for higher profit margins. However, deciding which crops to grow depends not only on the weather.

2. Market Demand Analysis

Specific crops offer high profits and yields in certain areas, and using AI farming, you can understand this.

For example, soybeans have high yields in Wisconsin, Tennessee, Ohio, North Dakota, and South Dakota. According to USDA, in September 2023, soybean processors produced 174.8 million bushels of soybeans.

You can build custom AI systems to analyze market data area-specific patterns and recommend the best crop to grow. Data-driven decision-making can help you improve ROI on your farming business.

3. Soil Health Monitorin

Monitoring the soil health is crucial to ensure high yield and long-term benefits. For a farmer, soil health is critical to a successful cropping season.

AI can help monitor soil health and recommend the fertilizers you need for optimal benefits.

Too many fertilizers damage soil health, which is why AI in farming makes sense. It helps reduce the impact of harmful chemicals on the soil used in fertilizers.

4. Automatic Harvesting

Harvesting robots that use AI models for guidance can harvest crops. These robots automatically harvest crops using multiple sensors, cameras, and a guide system that uses AI.

You can use six-axis robots for picking stationery crops, which can be paired with mobile units like drones or robotic transport units (RTUs). You can also leverage AI farming technology to determine which end effector will suit your harvesting needs.

Here is what to expect from automated harvesting

  • Sprays up to 12 acres in 9 hours
  • Fully autonomous navigation
  • Precision guidance (GPS+Camera+AI)

5. Weeding Efficiency

Another advantage of using AI for agriculture is automatic weeding. Smart robots with AI models at the core can remove weeds from the fields without manual labor.

Weeding robots leverage image recognition technologies with deep learning models to identify weeds between crops. These bots crawl over the crops and look for weeds with an end effector.

6. Real-time Data Analysis

Using artificial intelligence, farmers can access real-time data and insights into agricultural operations. For example, they can have real-time data on energy, water, and fertilizer usage.

Further, blending AI and farming operations allows farmers to improve productivity. Automating key activities and having real-time data on the performance helps improve productivity.

Knowing the benefits of using AI in farming, you may be tempted to invest in it. You may have even started a Google search on how to build an AI app. However, this is just the start, and AI farming technology will have more innovations in the future.

So, here are some interesting use cases to expect in the future of AI technology used in agriculture.

Maximize the benefits of automation in agriculture with tailor-made AI development services!

Future of Artificial Intelligence in Agriculture

As AI models advance, the use cases for farming will increase and span many different activities. For example, in future use cases of AI farming technology evolution, you can expect algorithms to help farmers automate 90% of tasks.

Other perks to expect from the future use of AI in agriculture are,

  • Precise Weather Forecasts- Farmers can access precise weather forecasts on their devices with localized AI on smartphones.
  • Renewable Farming- With the increased adoption of solar technology across the agricultural sector, using AI can improve energy efficiency.
  • Reduced Pollution- AI farming technology will allow farmers to reduce wastage and dispose of agricultural waste optimally. This will reduce the pollution caused by agricultural waste.
  • Crop Safety- Farmers can use AI to protect crops from wild animals through a system that includes motion sensors and cameras.
  • Automation Efficiency- Innovations in AI farming will improve the efficiency of automation, ultimately reducing manual efforts and increasing productivity.

By now, you know what to expect from AI in farming, but what about its practical usage?

Well, here are some case studies for you!

Real-world Examples of Artificial Intelligence in Agriculture

AI can be used in farming to optimize the entire operation, from disease detection to weed identification. Plantix is one such app that is transforming farming through AI.

Plantix Uses AI to Detect Plant Diseases For Farmers

This is an AI-based app that helps farmers identify more than 60 diseases. The app has a database of more than 100,000 photos of sick plants.

When farmers take a picture of the plants and upload it to the app, the AI algorithm compares the images with others in the database to identify disease types. It provides information about every disease affecting tomatoes, peppers, melons, squash, and lettuce.

Trace Genometics Devises an AI Test For Soil Monitoring

Matthew and Joe Schweigert, the owners of Schweigert family farms, partnered with Trace Genometics to create a test for soil monitoring on their farm. Soil samples are taken, and an AI-based system checks on different aspects of soil, like

  • Microbes
  • PH level
  • Moisture level
  • Nutritional deficiencies

The system creates a soil report based on the data analysis, identifying key aspects and areas where deterioration is increasing. Farmers can take preventive care of specific sections of their farms and reduce the impact of soil deterioration.

Harvesting Automation!

Evolving AI farming technology will transform the way agriculture operations are executed. From the moment farmers sow seeds to harvesting, every activity will be automated. Going forward, AI-based predictive maintenance and automation in farming will offer higher margins for farmers.

Integrating these capabilities, as Plantix did for a specific use case, will require expertise in app development. This is where Excellent Webworld can help you,

  • Custom app development
  • AI model customizations
  • AI integrations
  • Advanced feature development
  • API-based integrations

Contact us now to learn more about our AI-based app development services.

FAQs

AI is used in the farming sector to automate harvesting, weeding, predictive maintenance, and demand forecasting. It also helps farmers detect diseases and identify weather patterns.

Farmers can use generative AI capabilities to increase crop yields through data-driven recommendations, such as optimal fertilizer usage, irrigation, and harvest timing. AI can also provide climate impact reports and help identify the ideal harvesting time with the right pricing.

Farmers need training on the initial setup and operations of precision agriculture equipment with AI capabilities. They also need training on operating cameras and using apps and platforms for soil monitoring.

AI will improve farmers’ decision-making regarding which crops to grow based on weather conditions, market scenarios, and soil type. By analyzing a vast amount of data, AI will provide insights into farming operations.

AI can help the agriculture sector by reducing waste and improving profit margins. It can help farmers automate harvesting, weeding, and disease detection. Further, AI can improve productivity and yield, ensuring enough food is produced.

Paresh Sagar

Article By

Paresh Sagar

Paresh Sagar is the CEO of Excellent Webworld. He firmly believes in using technology to solve challenges. His dedication and attention to detail make him an expert in helping startups in different industries digitalize their businesses globally.