The Data Corner: Artificial Intelligence and Machine Learning Applications in Agriculture By Marlene Hanken, Data Analyst, Science Since the introduction of ChatGPT to the public earlier this year, Artificial Intelligence (AI) and Machine Learning (ML) have become hot topics for conversation. ML, a subtopic of AI, involves training algorithms (data models) on large amounts of data to enable them to make predictions or take actions based on the data. AI, on the other hand, refers to the ability of a machine or computer program to mimic intelligent human behavior—most notably problem solving, decision making and tasks that require human intelligence. Their applications and possibilities are endless.
To our growers, the most asked question is, “How can AI and ML be applied to agriculture?” While an exhaustive list of AI and ML applications in agriculture would be impossible to produce, this article will cover four applications to West Coast agriculture that have been underway prior to ChatGPT’s meteoric rise in attention. One of the most promising applications of AI and ML is precision farming. AI and ML algorithms can use field data on weather, soil conditions and plant growth to optimize crop yields. These algorithms can identify patterns and predict future crop growth and can set automated actions based on predefined threshold or benchmarks. By using precision farming, growers can optimize their use of resources, such as water, fertilizer and pesticides—resulting in increased crop yields, reduced water usage and overall costs and improvement of the efficiency of their operations. Another application of AI and ML is crop monitoring. Using cameras and sensors, growers can collect data on crop growth and health and feed these data to ML algorithms to identify early signs of disease or pests. Being able to detect these issues early, growers can take proactive measures to prevent them from causing significant damage to the crop. AI-powered tools, such as harvesters, can automatically remove affected crops from production upon detection. This is incredibly powerful and useful for crops susceptible to disease and pests. AI and ML are being used to improve efficiency of greenhouse operations. Greenhouses growing specialty crops can monitor temperature, humidity, light and other environmental factors. With these data, growers can optimize the conditions inside the greenhouse to improve crop yields and quality and set automatic responses to keep conditions optimal. Crops requiring more stringent conditions for growing are benefiting the most from this application. One of the most exciting applications of AI and ML is the development of autonomous tractors and
other farm equipment. Using the built-in sensors and cameras allows on-field data to be collected to aid in automated planting and harvesting equipment. Growers can fill labor shortages with some of these machines and reduce labor costs.
EXAMPLE ML USES • Prediction
• Image Recognition • Speech Recognition • Medical Diagnosis • Financial Industry & Trading
Source and Image: TowardsDataScience
Operations can implement AI and ML tools to improve the efficiency of supply chain management. By combining data from weather, crop growth and market demand, growers can make better decisions about when to plant and harvest their crops, optimize transportation schedules and better manage crop storage—all leading to better resource management. New applications are being discovered every day for this emerging technology. Are you using AI or ML in a way that wasn’t covered in this article? Contact us at data@wga.com—we’d love to learn more! Want to learn more about AI, ML and their applications in ag? Tune in to our Fall AI/ML webinar to learn more! Email us with questions related to this article or suggestions for future article topics.
10
JULY | AUGUST 2023
Western Grower & Shipper | www.wga.com
Made with FlippingBook Ebook Creator