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.
Tech Topic
AI
Farming System
Orchard
Application Category
ICT
Date
Nov. 2020