Deep learning for defected trees detection
farmB R&D team in cooperation with IBO’s Scientists prototyped a novel AI approach for the detection of trees with disease-infected leaves taken advantage of the latest developments in deep learning and object detection. The first disease type to be tested was anthracnose. So far, anthracnose machine learning based detection was performed on single leaf level and on images taken in controlled environment in terms of light and background clearness. The innovative approach is based on an object detector, namely a state-of-the-art Single Shot Detector (SSD), that was trained in detecting anthracnose-infected leaves on feature-rich images of walnut trees’ canopies under variable open-air orchard environment conditions.
Focus of this study was to build an accurate and fast object detection system that can identify anthracnose-infected leaves on walnut trees, in order to be used in real agricultural environments.
The encouraging results indicate that the detector shows great potential for direct application in commercial orchards, to detect infected leaves on tree level in real field conditions, and categorize the trees into infected or healthy in real time. Thus, this system can constitute an applicable solution for real-time scouting, monitoring, and decision making.