Multi Task Learning for Plant Leaf Segmentation and Counting

Abstract

In this paper, we propose an efficient method to segment the leaves and count the number of leaves in digital plant images, important tasks in plant phenotyping. However, for large cropping lands, automated leaf counting and segmentation are challenging and time-consuming due to the plant’s rapid growth, varying size of leaves and shape, and presence of occlusions, which pose the task challenging to even human experts. Deep learning methods show considerable improvement when employed in image-based plant phenotyping tasks like plant leaf counting and leaf segmentation. However, most of the research related to plant phenotyping was designed to perform a single-task at a time. Therefore, a multi-task deep learning (MTL) framework is proposed to infer the two tasks simultaneously (i)leaf segmentation and (ii)leaf counting. Compared to single-task learning, MTL achieved the segmentation mean intersection over union score of 0.7478 (improved by 2%), and the difference in the count is decreased by two times when employed in agricultural field images.