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. For a different way to engage, listen to the podcast below covering these key points.
What Is AI in Agriculture and How Does It Work?
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,
Now that you know what is AI in agriculture, it’s time to understand its impact.
Impact of AI on Modern Farming Practices
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.
Top 5 Benefits of Using AI in Agriculture
Artificial Intelligence, like agriculture technology, has advanced dramatically through continuous innovations and advancements. The marriage of AI and agriculture can be a cornerstone, especially considering the deteriorating food production and low farm yield. The soil that helps farmers produce massive amounts of food has specific chemical formations containing phosphorus, sulfur, potassium, calcium, and magnesium.
These minerals and micronutrients like copper, iron, manganese, and other elements offer fertility. This is where modern technology like AI becomes essential. AI technology can automatically determine what pesticides to apply and how much they will impact soil fertility. It can help improve soil fertility and reduce the impact of pesticide overuse on food production. However, this is not AI’s only benefit for the agriculture sector.
1. Data-driven Farming Decisions
In the current age of information-intensive markets, every business relies on data. One of the key benefits of AI in agriculture is its ability to analyze, process, and generate data. AI can answer questions such as what pesticide composition to use, how much to apply to fields, and which crop will yield more in a specific season.
Farmers and agriculture businesses are now using AI to predict crop yields and determine weather impact. In addition to weather predictions, agriculture businesses can use AI to analyze the market’s earlier prices and provide recommendations.
Moreover, farmers can use AI to gain insights into changing weather patterns. Farmers can use the weather data to decide which crops to sow and when. Agriculture businesses can use AI to optimize the logistics of market yield deliveries and improve profits.
AI-based data can help agribusinesses understand crop transportation and processing bottlenecks and address quality issues.
2. Cost-Effective and Efficient Farming
AI can boost farmers’ income by reducing costs and optimizing yield. Farmers can use AI to reduce the cost of pesticides, farming equipment, and wastage due to weather issues. AI provides intelligent recommendations to farmers on what equipment suits their farming needs and helps them understand the impact of extreme weather.
The cost-saving benefits of AI in agriculture also extend to other forms of farming. For example, insect farmers in Bangkok use AI to lower the cost of larvae farming. This is a crucial use case when larvae framing significantly reduces deforestation. Yes, soybean animal feed significantly impacts deforestation in South America. But with AI larvae, farmers are improving production and reducing costs.
When evaluating such adoption, farmers compare upfront investments with long-term savings. This is similar to how businesses assess the AI chatbot development cost before implementation.
Apart from larvae farming, salt farmers, fisheries, and other forms of farming can also leverage AI. A significant benefit of AI in agriculture is the real-time tracking of crops, which helps optimize fertilization, irrigation, and other aspects.
3. Automation and Smart Farming Benefits
Agriculture businesses can leverage AI to cope with and reduce the impact of labor shortages. See, agricultural activities have used automated machines before, and it’s not new. However, what AI brings to the table is the ability to integrate real-time data analytics.
AI combines the power of data-driven insights with precision automation, enabling farmers to achieve optimal outcomes. For example, farmers can leverage approaches like vertical farming. In this approach, crops are grown on vertical surfaces, which are stacked, producing more food.
Farmers can leverage AI to create specific vertical farming layers that can be integrated into modern buildings such as skyscrapers, greenhouses, and shipping containers. Further, farmers and agricultural businesses can leverage AI models to program clever spraying mechanisms for fertilizer application.
The possibilities of leveraging AI for automation in agriculture are endless, and as innovations grow, farmers will benefit more.
4. Promoting Sustainable Agriculture with AI
Ever since farming’s inception, sustainability has been a part of it. However, in recent years, humans have resorted to a more industrial approach to agriculture, which has impacted the environment. AI allows farmers to reduce farming’s environmental impact, boosting sustainability.
Sustainable farming can reduce energy usage per unit of crops produced by 56% and greenhouse gas emissions per hectare by 64%. Farmers can use ML-based algorithms for resource management and reduce environmental impact. For example, farmers can use precision water management(PWM) with AI.
PWM is a data-driven approach in agriculture that optimizes water usage. AI-based mechanisms automate water application precisely at the right time, place, and crop growth stage. This allows farmers to conserve more water, reduce energy usage, and boost sustainability.
5. Crop Inventory and Stock Management
A significant aspect of farming is stock management. Crops need proper handling, stocking, and storage to prevent damage. The farmer’s efforts and investment return will be zero if crops are damaged.
Agriculture businesses must manage stocks to reach the market as soon as possible. AI-based farm inventory management software can help farmers and agriculture businesses manage crop stock. Especially for temperature-sensitive crops, AI can help track the temperature of warehouses and storage areas. Further, using AI, farmers can automate temperature control to ensure crop quality.
These benefits make using AI in agriculture attractive for many farmers. However, using AI in agriculture can only be accessible if one knows the application and how it works for different use cases.
8 Practical Applications of AI in Agriculture
AI-enabled systems have been at the forefront of agricultural evolution. It has started empowering farmers with unique capabilities that foster growth and exponential yields. These capabilities include tracking crop diseases, weather patterns, and demand forecasting.
1. Smart Soil Monitoring and Analysis
Different regions have specific soil compositions. Farmers in LA County will have different soils, especially in terms of micro and macronutrients.
It is crucial to monitor crops at each growth stage and optimize production. This includes understanding how soil composition impacts crop growth. AI-based systems can help farmers track soil condition and its impact on crop growth.
Further, farmers can use drones to capture images of fields and leverage them to train computer vision models. This will provide farmers with real-time data on crop and soil conditions.
2. AI-Powered Crop Growth Tracking
Observing crop growth manually can be labor-intensive for farmers. AI can help automate crop growth tracking by using a computer vision model and cameras attached to sensors.
Researchers used a computer vision model with images from the wheat crop field. The photos of “heading” stages were captured and fed to the computer vision model to create a two-step coarse-to-fine detection system to detect wheat growth maturity.
Similarly, AI can help farmers identify whether a fruit or vegetable is ripe. A study on how an AI-based system can detect whether a tomato is ripe indicated an accuracy of 99.31%. AI-based observation and crop growth estimation are much better than labor-intensive manual tracking.
3. AI for Early Disease Detection in Crops
Another critical AI use case in agriculture is automatic crop disease detection. AI is making massive strides in crop disease detection and diagnosis. According to a study done by researchers to auto-detect apple scab, the accuracy of AI was 95%.
What makes the use of AI in agriculture exciting is how accurately it can detect diseases and reduce yield damage. One infected leaf or fruit can affect the entire yield. So, identification and timely diagnosis become essential for farmers. This is where AI can help accelerate diagnosis.
4. Automated Weed Detection and Control
A significant challenge for farmers is weeds, undesirable crops that grow in areas where farmers don’t intend to develop them. Weeds are unwanted plants that block space on a farm. They compete with regular crops for water, nutrients, and sunlight. Manual weeding is very labor-intensive and impacts farmers’ productivity.
AI-driven automated weed control systems leverage machine learning algorithms and computer vision to identify weeds accurately. Using AI in agriculture, farmers can optimize resource usage and improve ROI.
5. Smart and Automated Irrigation Systems
AI algorithms offer capabilities to agricultural systems to manage crop irrigation. By combining the Internet of Things (IoT) with custom AI models and sensors, farmers can monitor soil moisture and manage the water provided to the crops based on soil moisture data and weather conditions.
Autonomous crop irrigation with AI can help reduce water wastage and protect crops. Crops that require moderate irrigation can be protected from damage.
6. Leakage Detection in Farm Infrastructure
Leakages in irrigation systems are one of the common challenges for farmers. Because most irrigation systems run on electricity, such leakages can cause financial losses for farmers. Higher leakages lead to more water wastage and higher energy bills.
AI with an IoT-based system can help detect leakages in the irrigation system in real-time. This can save farmers energy and water, leading to better returns.
7. Predictive Demand Analysis for Crops
AI-based analytics can power farming businesses, helping them identify market demand for specific crops and plan production. Customized AI models can help analyze the market and suggest which crop will profit more. Blending this data with weather patterns and available resources offers a complete overview of what crops should be grown.
AI-based systems can also help farmers predict crop yield to understand if the production will meet market demand. This can be done through yield mapping. Using AI in agriculture allows farmers to implement crop yield mapping, which includes,
8. AI-Based Farm Surveillance and Monitoring
One crucial AI use case in agriculture is ensuring the security of the farm. Farmers often face security issues due to their farms’ geographical location. From stray cattle entering farms and damaging yields to thefts and other security issues, AI can help minimize risks.
AI-powered systems can help in,
Knowing how AI can be applied in agriculture is one thing, but implementing it is another. If the implementation is incorrect, the results that farmers perceive may not be achievable.
How to Optimize AI Implementation in Agriculture
AI has many benefits in agriculture but may not provide the best results if farmers can’t optimize the integration into existing processes. So here are some of the aspects of using AI for agriculture that farmers must optimize,
1. Ensuring High-Quality Agricultural Data
AI needs data to provide the insights farmers need. However, if the data is not of the right quality, the result will not be as expected. This is why it is crucial to combine AI with big data analytics. It allows farmers to get smarter recommendations and real-time data and improve productivity.
2. Efficient Farm Data Capturing Methods
Farmers need IoT sensors, cameras, and drones to capture data. These elements combine to monitor, capture, and store training data for AI models.
3. Training and Customizing AI Models
Many aspects of AI model training require technical expertise. Once the data is captured, information preparation for model training will commence. Farmers can leverage custom AI solutions for agriculture here.
4. Optimizing Farm Automation with AI
Using AI for agriculture with autonomous equipment like tractors and IoT-based systems can help optimize farming. AI helps farmers with advanced capabilities fused with robotics to offer intelligent automation. Lastly, AI adoption in agriculture also has several challenges that farmers must overcome.
6 Major Challenges of Using AI in Agriculture
The resistance to AI-based technology, especially outside the digital paradigm, stems from the idea that it’s not suitable for other forms of business. Understanding how AI can be used in agriculture becomes vital. It allows agriculture businesses to formulate new AI business ideas and create advanced tools for farming.
Agri-tech companies often need to communicate the benefits of AI in agriculture to farmers. While resistance to innovation remains a significant challenge, there are others.
1. Lack of Technical Skills Among Farmers
Farmers don’t have much AI experience, which is where agritech companies must focus. Many farmers are not well-equipped to implement the AI-based systems. A lack of digital skills and understanding of AI has become a significant roadblock.
Overcoming these roadblocks requires concentrated efforts to make farmers aware of AI’s benefits in agriculture. Agri-tech companies also need to invest in specific farmer training programs.
2. High Initial Investment in AI Tools
Developing an AI-based agricultural system requires higher upfront costs. This is due to the high infrastructure costs that farmers can’t afford. AI model development requires extensive training with big data. Farmers and agricultural businesses need massive infrastructure to store, process, and prepare data for AI model training.
While significant agriculture businesses can afford to invest on such a scale, small farmers and agro companies can’t.
3. Slow AI Adoption in Rural Farming
Due to resistance among the farming community against a new technology like AI, adoption takes longer. This has caused many agro-businesses to make higher investments, delay deployments, and lose money. To overcome these challenges, agritech businesses can adopt a more gradual approach with farmers.
4. Technology Limitations and Accessibility
A key challenge of using AI in agriculture in the current era has been tech restrictions. Several farmers operate in more remote settings where internet connectivity and the reach of technology have been restricted, creating a barrier to the adoption of AI in agriculture.
Overcoming this challenge required investments in setting up critical tech infrastructure. Agritech businesses must work with farmers to develop essential technology infrastructure and boost AI adoption.
5. Data Privacy and Security Concerns
Regulations on AI are still in their nascent stage and cause concerns for farmers regarding privacy issues. Implementing AI and ensuring precision farming need a regulated authority that determines how farmers’ data is used.
Plus, integrating public AI models can also mean farmers’ data can be accessed by any hacker who manages to infiltrate such systems. The best practice is to ensure the usage of AI in agriculture is regulated and ensures data privacy.
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,
Turning such possibilities into working solutions requires technical expertise. In this case, you can rely on AI development services to build tools aligned with real agricultural needs.
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 AI 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
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.
How AI in Agritech Solves Key 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.
AI in Agricultural Supply Chain 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: Labor Shortage in Farming
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-Driven Agricultural Labor Solutions
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: Unfavorable 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.
Using AI to Improve Farm Profitability
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.
Harvesting Automation: Final Insights on AI’s Role in Agriculture
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,
Get our AI consulting services to harness the power of AI in agriculture and stay ahead of future trends!
FAQs About AI in Agriculture
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.
Article By
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.
