More Cash From Your Crops: ML-Powered Agriculture
August 31, 2022
From targeted ads to malware detection, odds are AI and ML have been integrated into your life in some form. However, the field is still growing and expanding its reach into areas such as Agriculture. Global spending on smart, connected agricultural technologies and systems, including AI and machine learning, is projected to triple in revenue by 2025, reaching $15.3 billion. ML is shaping up to be the perfect tool to increase yield while minimizing water, land, fertilizer, and other chemical usages, but the question for most when they hear this is “how?”. Some companies make use of the field management applications and others utilize ML for quality control with crops and livestock. Below, we’ll dive deeper into some of the ML agricultural applications and techniques in use, as well as best practices to ensure your operations can take full advantage of them.
ML’s Growth Potential in Agriculture
The agricultural field is a $3.5 trillion global industry that supports over 27% of the worldwide workforce. However, the global demand for food is increasing at an alarming rate, raising the issue of sustainability and food security. To counter this challenge, approximately a 60% increase in food production is required by 2050, if we are to support the projected world population. The adoption of MLOps into agricultural production has become a necessity that the industry direly needs. ML has already started making an impact, providing insights that help increase productivity, using less water, fertilizer, pesticides, etc. The data processing abilities granted by ML have led to early adopters expanding the possibilities of crop growth into areas previously unsuitable and increasing overall yield.
Agricultural Challenges Meet ML Solutions
The agricultural industry is heavily dependent on a wide range of variables that dictate how crops perform over time. There are hundreds of thousands of crop species, each with its own set of suitable requirements of weather conditions, growth techniques, specialized fertilizers, and pesticides for maximum yield. This makes agriculture an industry that generates an enormous amount of data daily, ranging from field data on soil conditions to sensor data from monitoring equipment. ML has the ability to make sense of this constant flow of data and allow those in agriculture to see the impact each aspect has on production as a whole.
For example, ML can help determine how much fertilizer and moisture a specific section of crops need based on the real-time data being collected from field sensors during the growth cycle. The resource-intensive field of agriculture consumes inventory at a high rate, so employing ML models that can minimize consumption creates a direct line to increased productivity. ML can provide tools that allow industry decision-makers to make better use of their resources and employ techniques that improve crop yields and reduce the waste of resources.
Deployment Solutions That Bear Fruit
While many platforms for ML model deployment have been developed, they are not all created equal. Therefore it is important to choose a platform that allows users in the agriculture industry to tackle the unique challenges this sector faces. This industry’s ML adoption tends to be implemented with smart equipment run on the edge. Yet, agriculture is primarily based in rural environments where connectivity can be unreliable at best, creating a need for ML solutions that can be air gapped and utilize edge analytics to process data at the source. Also, the large and complex data sets common in agriculture can often be more than containerized solutions were designed to handle. Making platforms with the ability to incorporate large amounts of data like climate conditions and potential soil yield rates desperately needed.
Wallaroo is the last solution you’ll need for the last mile of ML. Our platform’s capacity for processing real-time data allows for quicker agricultural decision-making and more powerful insights into how changes are developing across the scope of your agricultural production. While utilizing containerized platforms can result in poor deployment performance as your business grows, Wallaroo’s scalability ensures consistent quality even in large-scale productions. Our platform is designed to perform in the most demanding environments, while still providing monitoring and explainability for your models in production.
To speak with an expert, please reach out to us at deployML@wallaroo.ai and learn more.
