KEYWORDS: RGB color model, Education and training, Cameras, Data modeling, Machine learning, Near infrared, Tunable filters, Multispectral imaging, Diseases and disorders, Optical filters
Cannabis pests such as spider mites, thrips and aphids cause enormous damage to crops. The lack of regulations regarding pesticides that can be used on cannabis, is a source of concerns for growers and the federal government since no regulations are yet in place. In order to favor prevention, as opposed to curing the problem, we developed a system based on convolutional neural networks and transfer learning techniques trained on multispectral images that is capable of detecting the early state of parasitic stress on cannabis plants instead of giving an operator the difficult task of visually detect whether the plant is already infected or not. This gives the grower time to remove pest-infected plants before they spread throughout the whole crop.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.