AI automation solves technological disparities---innovation is high now.

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

By 2050, we must produce 60 percent more food to feed a world population of 9.3 billion,  the Food and Agriculture Organization. Given the current industry challenges, doing that with a farming-as-usual approach could be tricky. Moreover, this would extend the heavy toll we already place on our natural resources.

This is where Artificial Intelligence can come to our rescue. The AI in Agriculture Market is  to grow from $1.7 billion in 2023 to $4.7 billion by 2028, highlighting the pivotal role of advanced technologies in this sector. This article explores three significant issues agriculture faces today and shows how AI is helping tackle them using real-world examples.

Three key challenges farmers face

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

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

2. Soil Quality and Irrigation: Soil degradation affects nearly  of the Earth's soil, diminishing its ability to grow crops, leading to a  of about $400 billion. Water scarcity and inefficient irrigation further dent agricultural output. Agriculture uses 70% of the world's accessible freshwater, but 60% of it is wasted due to leaky  systems.

3. Weeds: Despite advancements in agricultural practices, weeds cause significant declines in crop yield and quality. Around 1800 weed species reduce plant production by about 31.5%, leading to economic  of about $32 billion annually.

Artificial Intelligence is often used as a catchall phrase. Here, it refers to the systematic collection of data, pertinent 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 analytics. Let’s look at how AI helps address each of the above challenges:

1. Pest identification and control: Accurate, early 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 invasions and identify pests in the field.

For example, Trapview has  a device that traps pests and identifies them. It uses pheromones to attract pests, which are photographed by a camera in the device. By leveraging Trapview’s database, AI identifies over 60 pest species, such as the codling moth, which afflicts apples, and the cotton bollworm, which can damage lettuce and tomatoes.

Once identified, the system uses location and weather data to map out the likely impact of the insects and pushes the findings as an app notification to farmers. These AI-driven insights enable timely and targeted interventions, significantly reducing crop losses and chemical usage. Trapview  that its customers have seen a 5% increase in yield and quality, and overall savings of 118 million euro in growers’ costs.

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

Data from in-ground sensors, farm machinery, drones, and satellites are 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,  has built a platform specializing in soil health monitoring by leveraging real-time data to help users review and compare vital parameters alongside crop performance. Farmers gain insights into soil type and vegetation indices like 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  reduction in water usage, a 15% reduction in fertilizer usage, and up to 70% yield increase.

3. Weed Detection and Management: Precise identification and elimination of weeds is critical to preventing them from competing for precious resources with crops and minimizing herbicide use. Thanks to computer vision, drones and robots can now identify weeds amongst crops with high precision. This allows for targeted weed control, either mechanically or through precise herbicide application.