Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming

Abstract

Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this paper, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions --- a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.

Accepted for publication in IEEE Robotics and Automation Letters (RA-L), 2018

References in corpus (4)

• Fully Convolutional Networks for Semantic Segmentation
• A guide to convolution arithmetic for deep learning
• Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
• Towards Unsupervised Weed Scouting for Agricultural Robotics

Cited by in corpus (8)

• Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
• Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming
• Weed Density and Distribution Estimation for Precision Agriculture using Semi-Supervised Learning
• Deep Learning Techniques for In-Crop Weed Identification: A Review
• PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
• GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER
• Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs
• WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming