Improving smart forestry through machine learning
Smart-forestry solutions to optimize the efficiency of wood resources, supply chains.
As soon as this client started, they knew that they wanted to combine the AI with their other passion to understand the climate and how it affects our daily lives, but they were not yet sure how to bring it all together in a viable business.
Initially, their team planned to model and predict air quality in major cities in the same way that weather forecasting is done today. By combining massive amounts of proprietary municipal data – micro-weather, traffic, cameras – they knew they could build accurate models to predict air quality. Unfortunately, they soon realized that their plan had a flaw that killed many great ideas: a model of monetization that consumers or cities would be willing to pay for was missing.
Fortunately, their team quickly came across an underserved vertical in the forest industry where effective predictions of ML could have a huge impact - both on a company's financial results, but also on the environment itself through effective management of Natural Resources.
Timber supply chain forecast
One of their main products helps the forest industry to better understand and target the raw materials it buys. Essentially, it is a method of minimizing the risks associated with forest industry assets and transporting too many or a small number of trucks to the factories each day.
The company has focused its Services on major players in the industry, such as Forest funds that sell logs and large sawmills or pulp and paper producers that process logs. A modern pulp mill can have a supply chain of 300 or more trucks per day delivering logs. Making a mistake about the volume or quality of these shipments was a major problem that needed to be addressed.
Their main customers are buyers and sellers of wood-based raw materials (logs) who want to know the quantity and quality of wood entering the factories every day. Conventional prediction solutions for STEM and stem size, tree species and more struggle with a substantial margin of error. Traditional solutions also require someone to go into the woods and take manual measurements.
They solved the problem by giving the industry better forecasting accuracy and reducing the time spent on manual inspection.
In addition, uses a wide range of spatial, optical and other data sets to accurately predict wood quality and quantity. They have created a highly scalable cloud-based GIS solution that is available as a SaaS package, which provides real-time information on the market at the click of a mouse.
Rely on ROVE ANALYTICS - increases model development time by a factor of five!
Since its inception, this client has employed a combination of internal team members and a network of freelancers for its analytical work. Thanks to Rove Analytics, they were able to balance the model formation on several teammates while enjoying the peace of mind that Rove Analytics Inc automatic version control ensures nothing is ever lost on rotation. Our comprehensive historical data provides a clear map of what each of us did, regardless of whether the experiment was conducted a week or a decade ago.
They constantly test a wide range of algorithms and tunes for the best fit. Rove Analytics Inc allows teammates working on a problem to collaborate, try different strategies and learn from each other's experiences. The separation of the stages into machine learning streams also helped them to divide the work into image cleaning, data standardization, and then separate model training