Food is one of humanity’s most fundamental needs. The global demand for food is expected to rise 60% by 2050 to feed the world’s population. This is due to an increasing population, making our already poor use of resources even more unsustainable.
The need for food is currently met through farming. However, AI and machine learning in agricultural production is changing the industry and improving how food gets to our dinner tables. It can enhance decision-making accuracy and efficiency while lowering the risks and expenses associated with agriculture.
Today, governments, businesses, and farmers are using new technologies to secure access to the necessary water, energy, and land resources that could maintain adequate food stocks in the future. This includes AI and machine learning technologies, where global investments jumped from $3.4 billion in 2020 to $4.9 billion in 2021, an increase of 44%.
How Can AI and Machine Learning Boost Agriculture?
AI and Machine learning allow for real-time observation and monitoring of crops, fields, and workers. Thousands of granular data points in agriculture can be collected in terms of soil, fertilizers, water cycles, weather reports, pesticides, and more.
Without AI and machine learning, making sense of all these data points become an insurmountable task. Here are a few ways AI and machine learning can be used in agriculture:
1. Minimize Costs By Precision Spraying
Depending on the type of crop, precision spraying makes it possible to apply pesticides and fertilizers more precisely. Precision spraying guided by artificial intelligence uses the images and spectral fingerprints of vegetation, soil, and other materials to identify the best chemicals for a given crop. This is a great way to reduce herbicide use and costs. This method increases agricultural productivity while reducing crop damage. Some companies have managed to cut down the use of herbicides by more than 70% while lowering costs by more than half.
2. Maximize Harvest with Yield Mapping
With the help of yield mapping, farmers can identify differences in soil and moisture in different regions of their fields. This helps farmers determine what each field can produce. Typically, machine learning algorithms use data points from previous years. With the help of AI analysis and past agricultural trends, farmers can know what to expect in certain conditions.
3. AI-powered Drones
Every year, fewer people are employed in agricultural fields, leading to labor shortages. In this regard, drones can come in handy, especially in developed countries with large percentages of office workers and the infrastructure to employ robotic harvesters. Currently, there are two types of drones used in agriculture – multi-copter and fixed-wing.
The abilities of these drones include: quickly acquiring data, spraying chemicals safely, photography, and mapping. The data obtained can be transformed into a 3D model of the field, and fed into machine learning algorithms that can further analyze plant health and growth rates. Drones can help with harvesting as well.
4. Detecting Insects
Insects pose a serious hazard to crops. According to the FAO, insects, and pests reduce the global agricultural supply by 20–40% each year. When farmers use insecticides to protect their crops, they also often kill other helpful or neutral species in the process.
Manually identifying where pests are wreaking havoc is challenging and labor intensive. Drones have been used in agriculture to spot insects for years. Multispectral images captured from drones help identify crop stress, allowing farmers to zone in on areas that need attention.
5. Managing Field Conditions
Water and soil management are among the most crucial parts of farming. However, managing the different variables can be challenging. For this reason, farmers compile data on their property’s water and soil parameters. The data is later fed into a machine learning system, which subsequently provides suggestions for fertilization rates, pest management strategies, and irrigation schedules.
Additionally, machine learning is used to adjust soil sensors. The combined effects then assist with predicting water stress and nutritional shortages. Soil moisture can affect important farming operations such as crop selection, harvesting time, and tiling. To stay on top, farmers typically use weather information, soil and crop characteristics, and other factors to predict moisture. These forecasts are strengthened using experimental, predictive, and machine learning techniques, leading to better yields, lower costs, and more data-driven planning of water resources.
AI and Machine learning have become two of the most influential tools in agriculture, from simple analytics to sophisticated robotics equipment. As demand for food increases from population rise, more farmers are realizing the potential of these technologies and adapting them to safeguard future agriculture needs.
“It’s funny about this place. When I grew up, I hated farming. All I wanted to do was work on race-cars. Got to racing. Now all I want to do is to make enough money to work on a farm.” – Rowdy Burns, Days of Thunder (1990)