Weed control is essential in apple orchards because weeds compete with trees for nutrients, water and sunlight, which can reduce fruit yields. However, physically removing weeds is not only labor-intensive, but it also can damage soil structure and tree roots. Using chemical sprays to kill weeds can lead to other problems, such as pollution, herbicide resistance and excess chemical residues on apples. Another option called precision weed management - detecting and measuring weeds with high accuracy then applying small amounts of herbicide to control them efficiently - can help farmers avoid wasting chemicals or causing injury to crops or the environment, according to a team of researchers at Penn State. To help growers achieve such precise management, the researchers are developing an automated, robotic weed-management system.
The researchers reported on an early step in that process in the December issue of Computers and Electronics in Agriculture: an AI machine vision model they developed that can accurately find, outline, interpret and estimate the density of weeds in apple orchards. The system, intended to guide the eventual robotic precision sprayer, uses a machine vision innovation that allows a side-view camera to detect and identify weeds for treatment - even weeds that are partially obscured.
"In complex environments like apple orchards, it is difficult to develop weed-detection mechanisms because the tree canopy and low branches block the view from above, precluding traditional top-view camera systems, like drones, because they can't clearly see the weeds on the ground," said team leader and study senior author Long He, associate professor of agricultural and biological engineering. His research group in the College of Agricultural Sciences has been studying and developing robotic precision agricultural systems over the last decade. "A side-view camera can help, but weeds might be partially visible or hidden behind untargeted objects or tree trunks. This causes problems such as misidentifying weeds or losing track of a weed in real time."
To overcome those challenges, study first author Lawrence Arthur, doctoral candidate in the Department of Agricultural and Biological Engineering led the team in improving a commercially available deep-learning model for the machine vision computer program. The model was already capable of fast object detection and segmentation, meaning it can find the weed and outline its exact shape, pixel by pixel.
