Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming

TL;DR

This paper introduces a fully convolutional network that jointly detects weed stems and segments crops and weeds, enabling precise, plant-specific treatment in precision farming to reduce chemical use and environmental impact.

Contribution

It presents a novel end-to-end trainable model that simultaneously estimates stem locations and semantic segmentation, improving accuracy over existing methods.

Findings

Significantly improved stem detection accuracy.

Enhanced semantic segmentation performance.

Reliable performance on real-world datasets.

Abstract

Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to-end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.