Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain

TL;DR

This paper presents a hierarchical convolutional neural network for joint semantic, plant, and leaf instance segmentation in agriculture, improving accuracy and efficiency over existing methods using novel skip connections and post-processing.

Contribution

A unified CNN architecture that simultaneously performs semantic, plant, and leaf instance segmentation, leveraging hierarchical structure and novel post-processing for overlapping leaves.

Findings

Outperforms state-of-the-art methods in accuracy

Operates at camera frame rate

Uses fewer parameters

Abstract

Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accurately estimating plant traits such as the number of leaves, leaf area, and the plant size. In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data. We propose a single convolutional neural network that addresses the three tasks simultaneously, exploiting their underlying hierarchical structure. We introduce task-specific skip connections, which our experimental evaluation proves to be more beneficial than the usual schemes. We also propose a novel automatic post-processing, which explicitly addresses the problem of spatially close instances, common in the agricultural domain because of overlapping leaves. Our architecture simultaneously tackles these problems jointly in the agricultural context. Previous works either focus on plant or leaf segmentation, or do not optimise for semantic segmentation. Results show that our system has superior performance compared to state-of-the-art approaches, while having a reduced number of parameters and is operating at camera frame rate.