A holistic approach for identifying disease-infected trees within orchards was developed. Based on novel AI algorithms, it is capable of classifying images of single infected leaves, as well as detecting them on tree-level images, under variable open-air orchard environment conditions.
State of the art deep learning algorithms such as convolutional neural networks (CNN) for classification tasks and a Single Shot Detector (SSD) for the object detection, are utilized to provide accuracy in its predictions and high-speed performance on deployment.
Primary use case of the approach is the identification of walnut trees infected with anthracnose. Focus of the study was to build a robust system, able to operate accurately within operational environments, aiming towards a system, directly applicable in commercial orchards.
The encouraging results demonstrated that such a system is not only possible, but it can produce predictions of high level of accuracy. Such a system can be used for scouting and monitoring, as well as for providing inputs to Decision Support Systems (DSS) on whether and where, fungicide application is necessary.