Search

[Display omitted] • Deep learning-based apple tree health assessment system using image segmentation. • End-to-end implementation with a user-friendly interface. • The developed system was tested under field conditions in an apple orchard. • The platform allows to speed up the digital phenotyping process. Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution. [ABSTRACT FROM AUTHOR]