Side-view apple flower mapping using edge-based fully convolutional networks for variable rate chemical thinning

Abstract

Abstract Apple trees commonly require the removal of excessive flowers by thinning to produce high quality fruit. Machine vision has recently been applied to detect the flower density as the first step in this process. Existing work relying on color thresholding is sensitive to imaging conditions and the most recent published work using deep learning in this context has proven to be exceptionally slow to process. This paper presents an apple flower segmentation method on a pixel level based on a Fully Convolutional Network (FCN) together with a process of generating a map that can be used for a variable rate chemical sprayer. Despite the challenging conditions of an uncontrolled environment, our apple flower detector was able to generate a F1 score at pixel-level up to 85.6%, which is a relatively high accuracy in terms of pixel-level segmentation. Our method has been tested on both daytime and night-time datasets, which strongly validates the ability of our apple flower detector to work under different conditions. The resulting detections are georeferenced and merged into a density map in the format necessary for application by a variable rate chemical sprayer. Finally, this flower density mapping system will benefit farmers by visualising the whole crop and extracting useful information to support their decision making for chemical thinning.