AI innovations transforming agriculture

Agriculture is the cornerstone of human civilization, a testament to our ability to exploit nature for our livelihood. Yet this centuries-old industry faces numerous challenges that hamper productivity, impact livelihoods and threaten global food security.

By 2050, we need to produce 60 percent more food to feed a global population of 9.3 billion. reports the Food and Agriculture Organization. Given the current challenges in the sector, achieving this with a traditional agricultural approach could prove tricky. Furthermore, it would increase the heavy toll we already impose on our natural resources.

This is where Artificial Intelligence can come to our rescue. AI in the agricultural market is projected increase from $1.7 billion in 2023 to $4.7 billion by 2028, highlighting the central role of advanced technologies in this sector. This article explores three important problems facing agriculture today and shows how AI helps solve them using real-world examples.

Three major challenges facing farmers

Among the many issues affecting farmers, three stand out due to their global presence and financial impact:

1. Pests: Pests devour approximately 40% of global agricultural productivity each year, costing at least $70 billion. From swarms of locusts decimating fields in Africa to fruit flies affecting orchards, the impact is global and the financial repercussions are colossal.

2. Soil quality and irrigation: Land degradation affects nearly 33% of the Earth’s soil, diminishing its ability to grow crops, leading to a loss of around 400 billion dollars. Water scarcity and inefficient irrigation further harm agricultural production. Agriculture uses 70% of the world’s accessible fresh water, but 60% of this water is wasted through leaks. irrigation systems.

3. Weeds: Despite advances in agricultural practices, weeds cause a significant decline in crop yield and quality. Around 1,800 weed species reduce crop production by around 31.5%, leading to economic consequences losses approximately $32 billion per year.

How AI is transforming agriculture

Artificial intelligence is often used as a catch-all phrase. Here it refers to the systematic collection of data, relevant use of analytics ranging from simple descriptive summaries to deep learning algorithms, advanced technologies such as computer vision, Internet of objects and geospatial analysis. Let’s take a look at how AI helps address each of the challenges above:

1. Pest Identification and Control: Early, accurate identification and control of pests is essential to minimize crop damage and reduce the reliance on chemical pesticides. Data such as weather reports, historical pest activity, and high-resolution images captured by drones or satellites are readily available today. Machine learning models and computer vision can help predict pest outbreaks and identify pests in the field.

For example, Trapview has built a device that traps parasites and identifies them. It uses pheromones to attract pests, which are photographed by a camera built into the device. Using Trapview’s database, the AI ​​identifies more than 60 pest species, such as the codling moth, which attacks apples, and the cotton bollworm, which can damage lettuce and tomatoes.

Once identified, the system uses location and weather data to map the likely impact of insects and transmits the results as an application notification to farmers. These AI-driven insights enable timely and targeted interventions, significantly reducing crop losses and chemical use. Trap view reports that its customers saw a 5% increase in yield and quality, as well as an overall saving of €118 million in producer costs.

2. Soil health monitoring:Continuous monitoring and analysis of soil health is essential to ensure optimal growing conditions and sustainable agricultural practices. Optimizing water use is crucial to ensuring that crops receive precisely what they need, reducing waste and improving productivity.

Data from underground sensors, agricultural machinery, drones and satellites is used to analyze soil conditions, including moisture content, nutrient levels and the presence of pathogens. Such soil health analysis helps predict water needs and automate irrigation systems.

For example, CropX has built a platform specializing in soil health monitoring by leveraging real-time data to help users review and compare vital parameters as well as crop performance. Farmers get information on soil type and vegetation indices such as NDVI – Normalized Difference Vegetation Index, SAVI – Soil Adjusted Vegetation Index and Soil Moisture Index to optimize crop management strategies. CropX reports that its solutions have led to a 57% reduced water consumption, 15% reduction in fertilizer consumption and increased yield up to 70%.

3. Weed detection and management: Accurate identification and elimination of weeds is essential to prevent them from competing with crops for valuable resources and minimize the use of herbicides. Using computer vision, drones and robots can now identify weeds among crops with high accuracy. This allows targeted weed control, either mechanically or through precise herbicide application.

For example, startup Carbon Robotics leverages deep learning algorithms in its computer vision solution. He identifies weeds by analyzing data from more than 42 high-resolution cameras that scan fields in real time. Then it uses robotics and lasers to provide high-precision weed control.

The LaserWeeder claims to weed up to two acres per hour and remove up to 5,000 weeds per minute with 99% accuracy. Its producers report reducing weeding costs by up to 80% with a potential return on investment in one to three years.

Facing the risks of automation

AI has many benefits for agriculture, but it is not without risks, such as job displacement, ownership concentration and ethical concerns. When AI automates tasks traditionally performed by humans in large numbers, it could lead to job losses in manual and cognitive roles. Furthermore, it could exacerbate the concentration of ownership, benefiting large companies or wealthy individuals at the expense of small farms.

When farmland becomes a hotbed for data collection – underground, at crop level and from the sky, it could lead to data privacy issues. These challenges highlight the need for careful consideration and governance to balance the benefits of AI and its potential downsides. This is unique not only to the agricultural sector, but to all industries where AI is applied.

Ushering in a transformative future

The integration of AI in agriculture not only reshapes current practices but also paves the way for a sustainable and resilient future. AI could become a master gardener, constantly monitoring and adjusting every stage of the farm’s growth, from seed selection to harvest and beyond. It can help adapt agricultural practices in real time to climate change, ensuring optimal crop health and yield.