November 5, 2024 5 Ways in Which AI is Revolutionizing Agriculture

Agriculture is another key industry that’s been adopting AI to figure out solutions for the challenges it’s currently facing.

by Samantha Greene, Globy editor

Agriculture is another key industry that’s been adopting AI to figure out solutions for the challenges it’s currently facing. Climate change, soil desolation, water shortages, and nutrient loss are just a few of the problems the implementation of AI can help solve in the near future.

Although older generations of farmers are not willing to employ what many of them think is a gimmick, recent research conducted by Bushel has found “aggressive adoption” of technology by US farmers under 40. For them, using AI-powered software is not just a matter of convenience. It helps them manage costs and increase revenue.

Here are five other ways in which AI is changing the farming landscape worldwide.

1. Monitoring plants’ nutritional needs with AI and machine learning

A 2022 review article published in the Trends in Plant Science journal has revealed that rising carbon dioxide levels negatively impact crop yields and key mineral nutrients in plants. Crops grown on open land diminish in volume and show reductions in nitrogen, phosphorus, potassium, iron, zinc, magnesium, and sulfur. Thus, the rising CO2 levels influence both the quantity and quality of our food.

Farmers solve this problem by concentrated application of fertilizers. However, fuel and fertilizer prices are rising. It becomes less cost-effective to preemptively fertilize the whole planting area in the hope that every plant gets enough nutrients during all the stages of vegetation.

Enter the world of AI and machine learning. Researchers from Dalhousie University in Canada have offered to use spectroscopy to evaluate the nutritional composition of potato plants using machine learning techniques. The suggested technology allows for quick assessment of the plant’s nutritional needs and will ultimately help optimize the use of fertilizers, crop quality, and yields.

This new approach to fertilizing promises to be a valuable tool for all farmers, and not just potato growers. Spectroscopy and AI-based software will eventually help them find a balance between production goals and environmental protection.

2. AI-enabled automated irrigation for water savings

Severe droughts have been plaguing agriculture for quite some time, having a devastating effect on many crops such as olives, cocoa, rice, almonds, and others that require substantial amounts of water to thrive. Farmers can now tackle water scarcity using AI-powered software to analyze data from thermal drone imaging and weather forecasts and to properly calibrate automated irrigation systems.

Many big farms around the world already use agricultural drones to inspect farmlands and spot problems in irrigation systems such as leaks or clogged drippers. Drones with infrared cameras can also detect soil moisture levels and create land maps that help send water precisely where it’s needed.

As to more affordable AI-powered solutions, an Argentinian company called Kilimo has already achieved success in water savings by cooperating with farmers in Latin America. Kilimo’s AI-based software combines meteorological and satellite data and then suggests to the farmers whether or not to irrigate.

The company’s co-founder, Jairo Trad, has told the Guardian that farmers from Argentina, Brazil, Chile, Guatemala, Mexico, Peru, and Uruguay use the software, which has saved 72 million cubic meters of water in the past two years.

3. Weed management on large-scale farms

The widespread use of pesticides in industrial farming harms biodiversity and is unsustainable in the long run. A better alternative is offered by precision farming, a management strategy based on applying effort only where it’s necessary.

The future of weed management in precision agriculture lies in applying AI and machine learning to guide automated weeders. For example, the LaserWeeder, created by Carbon Robotics, literary shoots weeds with lasers. This massive robot uses machine learning combined with hardware to identify weeds and eliminate them one by one without harming the environment.

A less futuristic weeding technology now belongs to the agricultural machinery giant John Deer. Their weed sprayers get equipped with AI-trained cameras that can recognize weeds among crop plants. Thus, the farmer can apply herbicides to a specific area rather than the whole field, saving money on the herbicide and sprayer fuel and causing less harm to the environment.

4. Breeding new, climate-resilient plants

The basic approach to breeding new crop varieties has basically remained the same for the last 10,000 years. People look at plants, choose the ones with the most promising characteristics, and try to cross them to receive better-yielding and more resilient plants.

It’s called phenotyping, and the process is slow. It takes around 10 years to develop a new variety of plant, but with the fast onset of climate change, farmers just don’t have that much time. Luckily, AI can help with that as well.

The Artemis Project in Tanzania aims to make the phenotyping process ten times faster. Farmers participating in the project take photos of their crops through an app. The data is then analyzed by AI-powered models that help select plant genes that are best-adapted to specific locations and will be more resilient to climatic changes.

5. Yield Prediction and Supply Chain Optimization

AI enables farmers not only to improve production processes but also to make accurate yield forecasts, helping them plan sales and logistics more effectively. Yield prediction systems use data on weather conditions, soil quality, plant growth indicators, and past harvests. These data, processed by machine learning algorithms, allow farmers to predict crop volumes with high accuracy.

For farmers and agribusinesses, this means the ability to plan seasonal deliveries, adapt to shifts in product demand, and reduce storage costs. Optimizing supply chains helps minimize crop loss, provide stable income for farmers, and make food chains more resilient and predictable.

AI technologies are beneficial not only for managing production processes on farms but also for expanding access to international markets. The Globy platform uses AI to analyze market data and identify buyer needs, helping farmers find new partners around the world.

Conclusion

The integration of AI in agriculture is rapidly transforming traditional farming practices, offering innovative solutions to age-old challenges. From optimizing nutrient management to conserving water, reducing pesticide use, accelerating crop breeding, and improving supply chain efficiency, AI-driven technologies are making farming more sustainable, productive, and resilient to environmental changes. As adoption rates grow, especially among younger farmers, AI has the potential to redefine agriculture’s future, ensuring that farms worldwide can meet the demands of a growing global population while preserving natural resources.

About the Author:
Samantha Greene brings over a decade of experience in logistics and global trade, working with leading multinational corporations. Her vast understanding of supply chain dynamics and international compliance makes her a valuable asset.

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