“Of course, crops are constantly growing, moving in the wind, and changing color, etc., which makes it very difficult to automate the precise alignment of static images over time,” said Ph.D. student Jing Dong. “What we have been able to do is to account for the dynamic nature of continuously growing crops and animate a whole growing season’s worth of 3D images into a 4D reconstruction that reveals a bounty of useful information to farmers and other precision agricultural systems.”
The new computer vision-based method of autonomously monitoring agricultural crops may lower costs, improve harvest yields and provide more food to starving people around the world.