Predictability in agriculture is about as hard to find as a needle in a haystack. When you’re trying to prevent pest and disease pressures from devastating your yield, your approach to crop management needs to be proactive. But proactive management is impossible unless you can make good predictions and take appropriate steps in your fields.
Enter artificial intelligence (AI). AI is transforming industries around the globe, and agriculture is no exception. From game-changing data analysis to robust new prediction capabilities, AI in agriculture delivers key insights that can help you take proactive steps resulting in more productive crops and more efficient use of resources.
If you’re skeptical of how exactly AI is impacting crop production, we’ve compiled a few ways that AI in agriculture can streamline your operation.
AI in agriculture can enhance yield prediction accuracy
To determine an accurate yield prediction, you must account for soil conditions, environmental stressors, pest and disease pressure and management practices. Easier said than done.
Yield prediction has become more complicated with increasing seasonal variations due to volatile climate conditions.
AI can take yield prediction accuracy to the next level by leveraging technology and data to help you make informed decisions that benefit your operation, other agricultural stakeholders and the broader economy.
AI can be used to replace the typical mathematical, statistical and survey-based models that have been painstakingly compiled for yield prediction. AI has the ability to handle complex and nonlinear data effectively, using tools such as machine-learning algorithms to produce precise results for crop yield prediction. These yield prediction models can help farmers make decisions about what to grow and when to plant.
AI in agriculture increases operational efficiency
AI can help reduce labor costs within your organization and increase efficiency and profitability by automating repetitive tasks, setting up remote monitoring systems or even using autonomous vehicles for field tasks. AI can also help with resource allocation as you reduce human labor costs and keep boots on the ground where they are needed most.
AI in agriculture can improve pest identification and control
Minimizing crop damage from pests starts with early identification of the threat. AI can compile weather reports, past pest activity, and satellite imagery to help identify and predict pest invasions and identify pests in the field.
By efficiently compiling complex data, AI can help you map out potential insect populations to inform your pesticide strategy, allowing for more timely and targeted interventions to cut down on both crop losses and chemical costs.
Automated insect detection systems are another way that AI is streamlining pest identification and monitoring. After collecting heat, movement and sound readings, machine-learning algorithms examine and compare these data points to massive datasets for accurate identification of pests and recommended treatment plans.
AI in agriculture can improve weed identification and control
Similar to pest detection and identification, computer vision, drones and robots can help precisely identify weeds in fields. By singling out weed escapes, growers can use targeted mechanical or chemical control methods to eliminate weeds that compete with your crops for vital resources such as light, water and nutrients and minimize herbicide cost by limiting applications.
Experts are available to help you make your decisions. Reach out to your seed retailer, a nearby extension office agent, or a seed company professional like your regional BASF representative.
