AI innovation that transforms agriculture

Agriculture is the basis of human civilization and a testament to our ability to harness nature for food. However, this age-old industry faces many challenges that hinder productivity, impact livelihoods, and threaten global food security.

The Food and Agriculture Organization reports that to feed the world’s 9.3 billion people, more than 60 percent of the food will have to be produced by 2050. Given the current industry challenges, doing so with conventional farming approaches can be difficult. Moreover, this will add to the already significant strain we are placing on our natural resources.

This is where artificial intelligence can help us. AI in the agricultural market is predicted to grow from $1.7 billion in 2023 to $4.7 billion by 2028, highlighting the pivotal role of advanced technologies in this sector. . In this article, we examine his three key problems facing agriculture today and use real-world examples to show how AI can help solve them.

Three major challenges facing farmers

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

1. pest:Pests devour approximately 40% of global agricultural productivity annually, resulting in losses of at least $70 billion. From swarms of locusts destroying fields in Africa to fruit flies affecting orchards, the impact is global and the economic impact is enormous.

2. Soil quality and irrigation: Soil degradation affects nearly 33% of the earth’s soils, reducing their ability to grow crops and leading to losses of approximately $400 billion. Water scarcity and inefficient irrigation further reduce the value of agricultural production. Agriculture uses 70% of the world’s available fresh water, but 60% of that is wasted due to leaky irrigation systems.

3. weed: Despite advances in agricultural practices, weeds cause significant reductions in crop yield and quality. Approximately 1,800 weed species reduce plant production by approximately 31.5%, leading to economic losses of approximately $32 billion annually.

How AI will transform agriculture

Artificial intelligence is often used as a catchphrase. This refers to systematic data collection, the appropriate use of analytics ranging from simple descriptive summaries to deep learning algorithms, and advanced technologies such as computer vision, the Internet of Things, and geospatial analysis. Let’s take a look at how AI addresses each of the challenges above.

1. Pest identification and control: Accurate and early identification and control of pests is essential to minimize damage to crops and reduce dependence on chemical pesticides. Data such as weather forecasts, past pest activity, and high-resolution images taken by drones and satellites are available today. Machine learning models and computer vision can help predict pest infestations and identify pests in the field.

For example, Trapview has built a device to capture and identify pests. Pheromones are used to attract pests, which are captured by a camera inside the device. By leveraging Trapview’s database, AI identifies more than 60 pest species, including codling moth, which can damage apples, and cottontail moth, which can damage lettuce and tomatoes.

Once identified, the system uses location and weather data to plan for potential insect impacts and pushes the results to farmers as app notifications. These AI-powered insights enable timely and targeted interventions, significantly reducing crop losses and chemical use. Trapview reports that its customers have seen a 5% increase in yield and quality, and overall savings for growers of €118 million.

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

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

For example, CropX built a platform specifically for soil health monitoring leveraging real-time data, allowing users to see and compare key parameters along with crop performance. Farmers can gain insights into 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 solution reduces water usage by 57%, reduces fertilizer usage by 15%, and increases yields by up to 70%.

3. Weed detection and management: Accurate identification and removal of weeds is important to prevent competition with crops for valuable resources and to minimize herbicide use. Thanks to computer vision, drones and robots can now identify weeds in crops with high precision. This allows targeted weed control by mechanical or precise herbicide application.

For example, startup Carbon Robotics leverages deep learning algorithms in its computer vision solutions. Weeds are identified by analyzing data from over 42 high-resolution cameras that scan fields in real-time. In addition, robots and lasers are used to achieve high-precision weed control.

LaserWeeder claims it can weed up to 2 acres per hour and remove up to 5,000 weeds per minute with 99% accuracy. Growers report cutting weed control costs by up to 80% and seeing a return on investment in one to three years.

Tackling automation risks

Although AI offers many benefits for agriculture, it is not without its own risks, such as job losses, concentrated ownership, and ethical concerns. As AI automates tasks traditionally performed heavily by humans, it can lead to job losses in both manual and cognitive roles. Additionally, it could exacerbate ownership concentration, benefiting large corporations and the wealthy at the expense of small farms.

As agricultural fields become hotbeds for data collection from underground, crop level, and from the air, it can lead to data privacy issues. These challenges highlight the need for careful consideration and governance to balance the benefits and potential drawbacks of AI. This is specific not only to the agricultural sector, but to all industries where AI is applied.

Leading to a transformative future

Integrating AI in agriculture will not only reshape current practices but also pave the way for a sustainable and resilient future. AI could become a master gardener, continuously monitoring and fine-tuning every stage of growth on your farm, from seed selection to harvest and beyond. It helps you adjust agricultural practices in real time to changing climate to ensure optimal crop health and yield.

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